Power efficiency based on a dynamic report rate

ABSTRACT

A method and system configured to receive a first report from a computer peripheral device by a receiver, determine that the first report is corrupted or received at a rate slower than the first report rate, compute a current trajectory of the computer peripheral device based on one or more intervals of movement data in the first report, compute a predicted trajectory of the computer peripheral device based on the first report, compute an incremental displacement of the computer peripheral device based on the predicted trajectory. The method and system can further generate data indicative of a position or displacement of the computer peripheral device based on the predicted trajectory of the computer peripheral device and send the data indicative of a position or displacement of the computer peripheral device at an interval that is less than twice a period of the first report rate to the host computing device.

CROSS-REFERENCES TO RELATED APPLICATIONS

This application is a continuation of U.S. Non-Provisional applicationSer. No. 16/888,164, filed May 29, 2020, titled “IMPROVED POWEREFFICIENCY BASED ON A DYNAMIC REPORT RATE,” which is herein incorporatedby reference in its entirety for all purposes.

The following two U.S. patent applications listed below (which includesthe present application) were filed concurrently, and the entiredisclosures of the other applications listed below are incorporated byreference into this application in their entirety for all purposes:

-   -   application Ser. No. 16/888,164, filed May 29, 2020, entitled        “IMPROVED POWER EFFICIENCY BASED ON A DYNAMIC REPORT RATE.”    -   application Ser. No. 16/888,184, filed May 29, 2020, entitled        “PREDICTIVE PERIPHERAL LOCATING TO MAINTAIN TARGET REPORT RATE.”

BACKGROUND

Currently there are a wide variety of peripheral electronic devices thatare configured to communicate with a host device including mice,keyboards, headsets, trackballs and the like. Many of these peripheraldevices employ wireless communications protocols to provide the userwith flexibility and convenience, however traditional wirelesscommunications protocols can experience a relatively significant timedelay between when a user interacts with a peripheral device and whenthe host recognizes the interaction (also referred to as “latency”herein). Latency can be especially important in e-sports and competitivegaming, where fast report rates (e.g., <5 ms) are often expected in highend gaming peripheral devices, such as wireless computer mice. However,fast report rates (e.g., human interface commands, or “HID” commands)are often associated with a corresponding high power consumption thatmay result in low battery life in some wireless peripheral devices,which may be unacceptable for more discerning gamers.

Furthermore, in some instances, reliably maintaining a fast report ratemay be challenging due to wireless interference, jitter, or otherdeleterious phenomena, which can cause report rates to vary beyondcertain desirable specifications. As such, improvements in powerconsumption and reliability are needed, particularly in high performanceperipheral devices with fast reporting rates.

Unless otherwise indicated herein, the information and materialsdescribed in this section is not prior art to the claims in thisapplication and are not admitted to be prior art by inclusion in thissection.

BRIEF SUMMARY

This summary is not intended to identify key or essential features ofthe claimed subject matter, nor is it intended to be used in isolationto determine the scope of the claimed subject matter. The subject mattershould be understood by reference to appropriate portions of the entirespecification of this disclosure, any or all drawings, and each claim.

In certain embodiments, a computer-implemented method comprises:controlling, by one or more processors, a motion sensor (e.g., anoptical sensor, accelerometer, etc.) disposed on a computer peripheraldevice, the optical sensor configured to generate movement data atintervals, the movement data corresponding to a tracked movement of thecomputer peripheral device with respect to an underlying surface;sending, by the one or more processors, a first report to a receivercoupled to a host computing device at a first report rate, the firstreport including one or more intervals of generated movement data, thefirst report being sent via a wireless communications protocol;receiving the first report by the receiver; determining, by thereceiver, that the first report is corrupted or received at a rateslower than the first report rate; computing, by the receiver, a currentvelocity and acceleration of the computer peripheral device based on theone or more intervals of movement data in the first report; predicting,by the receiver, a trajectory of the computer peripheral device based onthe first report; computing, by the receiver, an incrementaldisplacement of the computer peripheral device based on the predictedtrajectory; generating, by the receiver, data indicative of a positionor displacement of the computer peripheral device based on the computedcurrent velocity and acceleration of the computer peripheral device; andsending, by the receiver to a host computing device, the data indicativeof a position or displacement of the computer peripheral device at aninterval that is less than twice a period of the first report rate. Themovement data can be velocity data, acceleration data, or datacorresponding to a change in an angle of movement.

In some implementations, the predicting the trajectory of the computerperipheral device can be performed by a state estimator operating on thereceiver, which can be a linear state estimator, a Kalman filter, anextended Kalman filter, etc., that is configured to incorporate anestimated error to balance error correction of the computed currentvelocity and acceleration of the computer peripheral device. In somecases, the method can include controlling, by the one or moreprocessors, an inertial measurement unit (IMU) on the computerperipheral device, the IMU configured to generate acceleration datacorresponding to the tracked movement of the computer peripheral devicewith respect to the underlying surface. In some embodiments, theperipheral computing device can be a computer mouse, and the receivercan be a USB dongle physically and communicatively coupled to the hostcomputing device. Other computer peripheral devices, receivers, and hostcomputing devices can be used, as further described in the detaileddescription below. In some aspects, the first report rate is 6 ms orfaster.

In some embodiments, a computer-implemented method comprises: receivinga first report from a computer peripheral device by a receiver at afirst report rate, the first report including one or more intervals ofmovement data generated by the computer peripheral device, the movementdata corresponding to a tracked movement of the computer peripheraldevice with respect to an underlying surface, and the first report beingreceived via a wireless communications protocol; determining, by thereceiver, that the first report is corrupted or received at a rateslower than the first report rate; computing, by the receiver, a currenttrajectory of the computer peripheral device based on the one or moreintervals of movement data in the first report; computing, by thereceiver, a predicted trajectory of the computer peripheral device basedon the first report; computing, by the receiver, an incrementaldisplacement of the computer peripheral device based on the predictedtrajectory; generating, by the receiver, data indicative of a positionor displacement of the computer peripheral device based on the predictedtrajectory of the computer peripheral device; and sending, by thereceiver to a host computing device, the data indicative of a positionor displacement of the computer peripheral device at an interval that isless than twice a period of the first report rate. In some cases, themovement data includes velocity and/or acceleration data, and thecurrent trajectory can be computed based on the velocity and/oracceleration data. In certain aspects, the computing the predictedtrajectory of the computer peripheral device can be performed by a stateestimator operating on the receiver, which can be a linear stateestimator, a Kalman filter, or an extended Kalman filter configured toincorporate an estimated error to balance error correction of thecomputed current trajectory (which can be based on the current velocityand acceleration) of the computer peripheral device. The first reportrate is typically 6 ms or faster (in some cases, 1 ms or faster). Themethod can further include controlling an inertial measurement unit(IMU) on the computer peripheral device, the IMU configured to generateacceleration data corresponding to the tracked movement of the computerperipheral device with respect to the underlying surface. In someembodiments, the peripheral computing device can be a computer mouse,and the receiver can be a USB dongle physically and communicativelycoupled to the host computing device, although types of peripheralcomputing devices, receivers, and host computing devices may be used, asfurther described in the detailed description below.

In further embodiments, a system comprises one or more processors andone or more machine-readable, non-transitory storage mediums thatinclude instructions configured to cause the one or more processors toperform operations including: receiving a first report from a computerperipheral device by a receiver at a first report rate, the first reportincluding one or more intervals of movement data generated by thecomputer peripheral device, the movement data corresponding to a trackedmovement of the computer peripheral device with respect to an underlyingsurface, and the first report being received via a wirelesscommunications protocol; determining, by the receiver, that the firstreport is corrupted or received at a rate slower than the first reportrate; computing, by the receiver, a predicted trajectory of the computerperipheral device based on the one or more intervals of movement data inthe first report; computing, by the receiver, a predicted trajectory ofthe computer peripheral device based on the first report; computing, bythe receiver, an incremental displacement of the computer peripheraldevice based on the predicted trajectory; generating, by the receiver,data indicative of a position or displacement of the computer peripheraldevice based on the predicted trajectory of the computer peripheraldevice; and sending, by the receiver to a host computing device, thedata indicative of a position or displacement of the computer peripheraldevice at an interval that is less than twice a period of the firstreport rate. The movement data can include velocity and/or accelerationdata, and the predicted trajectory is computed based on the velocity oracceleration data. The computing the predicted trajectory of thecomputer peripheral device may be performed by a state estimatoroperating on the receiver, which can include a linear state estimator, aKalman filter, or an extended Kalman filter configured to incorporate anestimated error to balance error correction of the computed trajectory(e.g., based on the current velocity and/or acceleration) of thecomputer peripheral device.

The first report rate is typically 6 ms or faster (in some cases, 1 msor faster). The method can further include controlling, by the one ormore processors, an inertial measurement unit (IMU) on the computerperipheral device, the IMU configured to generate acceleration datacorresponding to the tracked movement of the computer peripheral devicewith respect to the underlying surface. In some embodiments, theperipheral computing device can be a computer mouse, and the receivercan be a USB dongle physically and communicatively coupled to the hostcomputing device, although types of peripheral computing devices,receivers, and host computing devices may be used, as further describedin the detailed description below.

The foregoing, together with other features and examples, will bedescribed in more detail below in the following specification, claims,and accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The features of the various embodiments described above, as well asother features and advantages of certain embodiments of the presentinvention will be more apparent from the following detailed descriptiontaken in conjunction with the accompanying drawings, in which:

FIG. 1 shows an example of a computing system that can include any of avariety of host computing devices and peripheral devices, withperipheral devices that can be configured to perform aspects of thevarious inventive concepts described herein.

FIG. 2 shows a system for operating a peripheral input device, accordingto certain embodiments.

FIG. 3 is a simplified block diagram of a computing device, according tocertain embodiments.

FIG. 4A shows aspects of a receiver system configured to communicativelycouple a peripheral device to a host computing device, according tocertain embodiments.

FIG. 4B shows aspects of a receiver system configured to communicativelycouple a peripheral device to a host computing device, according tocertain embodiments.

FIG. 5 is a graph showing a relationship between a report rate and amaximum battery life for a peripheral device, according to certainembodiments.

FIG. 6 is a swim lane diagram showing aspects of a method of predictinga displacement of a peripheral device to achieve an increased reportrate and lower power dissipation, according to certain embodiments.

FIG. 7 is a simplified flow chart showing aspects of a method forpredicting a displacement of a peripheral device to achieve an increasedreport rate and lower power dissipation, according to certainembodiments.

FIG. 8 is a simplified flow chart showing aspects of a method forpredicting a displacement of a peripheral device to achieve a targetreport rate, according to certain embodiments.

FIG. 9 is a simplified flow chart showing aspects of a method forpredicting a displacement of a peripheral device to achieve a targetreport rate, according to certain embodiments.

FIG. 10 is a simplified flow chart showing aspects of a method fordynamically controlling a report rate on a computer peripheral device,according to certain embodiments.

FIG. 11 shows a graph 1100 depicting an extrapolation of an XY reportfor a computer peripheral device, according to certain embodiments.

FIG. 12 is a simplified flow chart showing aspects of a method forpredicting a position of displacement of a computer peripheral devicewhen missing or corrupted reports occur, according to certainembodiments.

FIG. 13 is a simplified flow chart showing aspects of a method fordynamically controlling an operation of a computer peripheral devicebased on a predicted activity level, according to certain embodiments.

FIG. 14 is a simplified flow chart showing aspects of a method fordynamically controlling an operation of a computer peripheral devicebased on a current detected movement, according to certain embodiments.

FIG. 15A shows a system with a receiver configured to perform apredictive analysis of a movement of a peripheral device, according tocertain embodiments.

FIG. 15B shows a system with a host computing device configured toperform a predictive analysis of a movement of a peripheral device,according to certain embodiments.

FIG. 15C shows a system with a remote server (e.g., on the cloud)configured to perform a predictive analysis of a movement of aperipheral device, according to certain embodiments.

FIGS. 16A-16C show examples of different applications of the predictivetechniques described herein, according to certain embodiments.

Throughout the drawings, it should be noted that like reference numbersare typically used to depict the same or similar elements, features, andstructures.

DETAILED DESCRIPTION

Aspects of the present disclosure relate generally to computerperipheral devices, and more particularly to systems and methods fordynamic report rate generation and location prediction for a peripheraldevice, according to certain embodiments.

In the following description, various examples and implementations ofpredicting a movement of a computer peripheral device are described. Forpurposes of explanation, specific configurations and details are setforth in order to provide a thorough understanding of the embodiments.However, it will be apparent to one skilled in the art that certainembodiments may be practiced or implemented without every detaildisclosed. Furthermore, well-known features may be omitted or simplifiedin order to prevent any obfuscation of the novel features describedherein.

The following high level summary is intended to provide a basicunderstanding of some of the novel innovations depicted in the figuresand presented in the corresponding descriptions provided below. Aspectsof the invention relate to systems and methods directed to dynamicreport rate generation and location prediction for a peripheral device.For instance, some embodiments can predict a location of a peripheraldevice to improve certain aspects of peripheral device performance. Toillustrate the advantages of such embodiments, consider that somecontemporary peripheral devices, such as computer mice, have advanced inperformance capabilities over the years as quick peripheral devicereaction times (e.g., due to fast report rates) have become expected inhigh end systems, particularly with competitive gaming communities(e.g., e-sports competitors). Report rates (e.g., human interface device(HID) reports) as fast as 1 ms can be expected in high performanceperipheral devices, which can be challenging to maintain when multipleperipheral devices (e.g., gaming grade computer mouse, keyboard,headset, speakers, etc.) are wirelessly coupled to and simultaneouslysending data to the host device. For example, certain deleteriouseffects such as jitter or signal interference may cause one or morereports to be corrupted (e.g., contain indecipherable or unusable data)or lost (e.g., missing packets such as token, data, handshake, orstart-of-frame packets), which can temporarily and/or periodicallyreduce the report rate below the target rate (e.g., 1 ms). Furtherstill, deleterious effects aside, maintaining fast report rate can posea significant power draw for wireless devices. For example, theimplementation of wireless communication for peripheral devices (e.g.,computer mice) may typically be designed to account for approximately10% of a total power draw from a local battery. High performance deviceswith fast report rates may account for up to 40% of the total powerbudget, which can markedly reduce battery life in these devices. Aspectsof the invention are directed to solving and/or remedying these issuesand more by using position prediction for a peripheral device tosupplement reports and increase/maintain report rates, as furtherdescribed in the embodiments that follow.

To illustrate some advantages of using location prediction, considerthat some peripheral devices (e.g., computer mice) may submit reports(e.g., one report or an aggregation of reports) to a wireless receivercoupled to a host computer device (see, e.g., FIG. 4 ) at a first reportrate (e.g., 8 ms) to, e.g., control a cursor or other video element(e.g., game-based aiming reticle) on a display. Some embodiments maydetermine a current trajectory of the peripheral device based on thereport (e.g., typically including human interface device (HID) commands)and calculate a prediction of a location of where the peripheral devicewill be at a later time (e.g., 1 ms later). In some cases, theprediction can be done at a markedly faster rate than the report isreceived (e.g., predication calculated every 125 us, 250 μs, 500 μs, 1ms, etc., as opposed to a report rate of 8 ms) and can be used tosupplement the report rate to provide a location of the peripheraldevice at a faster rate. In some embodiments, a new report may becreated based on the predicted position of the peripheral device, whichcan in turn be used to control the video element at the faster reportrate (e.g., 1 ms instead of 8 ms). The predicted position can beadjusted/corrected when the next report comes at the next 8 ms intervalwith the next actual current location of the peripheral device. By usingthese predictive techniques, faster report rates can be achieved, whichcan improve power requirements and report rate reliability, among otherbenefits. For example, a computer mouse may provide a report rate every8 ms, but a revised report (e.g., a calculated actual position of theperipheral device in addition to a predicted future position orpositions) can be send to the host computing device every 1 ms. Theserevised reports with predicted position data can significantly reduce anamount of power required by the peripheral device when generatingreports because the report rate can be markedly reduced. In anotherexample, a report may be received from a peripheral device every 1 ms,but may occasional drop below 1 ms due to interference, lost packets,etc. In such cases, a predicted position of the peripheral device can beused to supplement the actual position data to maintain, or in somecases, increase the report rate as predicted positions can be calculatedat a faster rate than the incoming reports are received.

In other embodiments, the predictive techniques described herein can beused to dynamically control the operation of a computer peripheraldevice. More specifically, a report rate of a computer peripheral devicemay be dynamically controlled based on its usage. For example, when thecomputer peripheral device is determined to be operating relativelyslowly (e.g., below a threshold velocity, acceleration, or angle ofmovement) or it is determined that the computer peripheral device isoperating software on the host computing device that does not requirehigh performance (e.g., a word processing application), then a receiver(or host computing device, cloud computing entity, network entity, etc.)can generate and send a command to the computer peripheral devicecausing it to operate at a reduced report rate (e.g., 4-10 ms reportrate or slower, reduced sensor polling rate, or other reduction).Conversely, when the computer peripheral device is operating relativelyquickly (e.g., at or above a threshold velocity, acceleration, or angleof movement) or it is determined that the computer peripheral device isoperating software on the host computing device that does require highperformance (e.g., e-sports games), then a receiver (or host computingdevice, cloud computing entity, network entity, etc.) can generate andsend a command to the computer peripheral device causing it to operateat an increased report rate (e.g., 1 ms report rate or faster, increasedsensor polling rate, or other performance enhancement). There are manyadditional advantages, some of which are discussed below, as would beappreciated by one of ordinary skill in the art with the benefit of thisdisclosure.

Some embodiments may perform the various predictive techniques describedherein at a wireless receiver (e.g., a USB dongle) coupled to a hostcomputing device that wirelessly receives data (e.g., reports) from theperipheral device, as lost packets are often the result of someinterference or anomalous effects that occur during transmission ratherthan via hardwired connections. Although many of the embodimentsdescribed below are configured this way, it should be understood thatthe predictive techniques may be performed by the peripheral device, bythe host computing device, or a combination thereof, which may depend onthe application of the predictive techniques (e.g., power reduction,report rate management, etc.).

In some aspects, velocity and acceleration data for a peripheral device(e.g., a displacement of a computer mouse along an underlying surface, arotation of a scroll wheel, etc.) can be used to calculate a currenttrajectory and predict where the peripheral device will be at a certaintime or interval (e.g., 1 ms later) based on the current trajectory.Velocity data can be received from a motion sensor, optical sensor, halleffect sensor, or the like, and acceleration data may be received froman inertial measurement unit (IMU), which may include an accelerometer,for example. In some embodiments, the predicting can be performed by astate estimator, which may incorporate a linear state estimator, KalmanFilter (KF), or an extended Kalman filter (EKF) configured toincorporate an estimated error to balance error correction of thecomputer current velocity and acceleration of the computer peripheraldevice that were previously predicted. Further details are provided inthe embodiments that follow.

FIG. 1 shows an example of a computing system 100 that can include anyof a variety of host computing devices and peripheral devices, includingperipheral devices (e.g., a computer mouse) that can be configured toperform aspects of the various inventive concepts described herein.Computing system 100 shows a host computing device (shown as a desktopcomputer) 110 and a number of peripheral devices communicatively coupledto the host computing device, include a display device 120, a computermouse 130, and a keyboard 140. Although the host computing device isshown as a desktop computer, other types of host computing devices canbe used including gaming systems, laptop computers, set top boxes,entertainment systems or any other suitable host computing device (e.g.,smart phone, smart wearable, or the like). In some cases, multiple hostcomputing devices may be used and one or more of the peripheral devicesmay be communicatively coupled to one or both of the host computingdevices (e.g., a mouse may be coupled to multiple host computingdevices). A host computing device may be referred to herein as a “hostcomputer,” “host device,” “host computing device,” “computing device,”“computer,” or the like, and may include a machine readable medium (notshown) configured to store computer code, such as driver software,firmware, and the like, where the computer code may be executable by oneor more processors of the host computing device to control the one ormore peripheral input devices.

A typical peripheral device can include any suitable input peripheral,output peripheral or input/output peripheral device including thoseshown or any peripheral device not shown (e.g., game controller, remotecontrol, wearables (e.g., gloves, watch, head mounted display), AR/VRcontroller, stylus device, gaming pedals/shifters, or the like. Aperipheral device may be referred to as an “input device,” “peripheralinput device,” “peripheral,” or the like. It should be understood thatalthough the majority of embodiments depicted herein are focused onapplications involving computer-related peripheral devices (e.g.,specifically computer mice), those of ordinary skill in the art wouldunderstand how to adapt the concepts applied to non-computer relatedapplications. FIGS. 2-4B and the corresponding description providetypical and non-limiting system block diagrams for a peripheral device,host computing device, and wireless receiver, respectively. FIG. 5-7present examples of using the various predictive techniques describedherein to both improve a power efficiency of a computer peripheraldevice and achieve an increased report rate. FIGS. 8-14 present examplesof generating reports based on predicted movement to compensate ormissing and/or corrupted reports from a computer peripheral device, andto achieve a target report rate. FIG. 16A-C show a few examples of howthe predictive techniques described herein can be applied to variousinput elements on a number of different computer peripheral devices.

FIG. 2 shows a system 200 for operating a peripheral input device,according to certain embodiments. System 200 may be configured tooperate any of the peripheral devices specifically shown and describedherein (e.g., keyboard 140, mouse 130, speakers (not shown), display120, etc.) or peripheral not shown (e.g., IoT devices) but within thewide purview of the present disclosure. System 200 may includeprocessor(s) 210, memory 220, a power management system 230, acommunication system 240, an input detection module 250, and an outputcontrol module 260. Each of the system blocks 220-260 can be inelectrical communication with the processor(s) 210 (e.g., via a bussystem). System 200 may also include additional functional blocks thatare not shown or discussed to prevent obfuscation of the novel featuresdescribed herein. System blocks 220-260 may be implemented as separatemodules, or alternatively, more than one system block may be implementedin a single module. In the context described herein, system 200 can beincorporated into any peripheral device described herein and may beconfigured to perform any of the various methods of controlling visualoutput elements on one or more peripheral devices, host computingdevices, or the like, as would be appreciated by one of ordinary skillin the art with the benefit of this disclosure.

In certain embodiments, processor(s) 210 may include one or moremicroprocessors and can be configured to control the operation of system200. Alternatively, processor(s) 210 may include one or moremicrocontrollers (MCUs), digital signal processors (DSPs), or the like,with supporting hardware and/or firmware (e.g., memory, programmableI/Os, etc.), as would be appreciated by one of ordinary skill in theart. Processor(s) 210 can control some or all aspects of operation ofinput device 130 (e.g., system block 220-260). Alternatively oradditionally, some of system blocks 220-260 may include an additionaldedicated processor, which may work in conjunction with processor(s)210. Processor(s) 210 may be local to the peripheral device (e.g.,contained therein), may be external to the peripheral device (e.g.,off-board processing, such as by a corresponding host computing device),or a combination thereof. As further described below, processor 302 ofFIG. 3 may work in conjunction with processor 210 to perform some or allof the various methods (e.g., methods 600-900, etc.) describedthroughout this disclosure. One of ordinary skill in the art wouldunderstand the many variations, modifications, and alternativeembodiments that are possible. Processor(s) 210 may perform any of thevarious functions and methods described and/or covered by thisdisclosure, and may operate to generate the various commands (e.g., HIDcommands, packetize streamed or aggregated data into reports to be sentat a report rate, etc.), in conjunction with any other resources/blocksin system 200) and corresponding functions described herein.

Memory 220 can store one or more software programs to be executed byprocessors (e.g., in processor(s) 210). It should be understood that“software” can refer to sequences of instructions that, when executed byprocessing unit(s) (e.g., processors, processing devices, etc.), causesystem 200 to perform certain operations of software programs. Theinstructions can be stored as firmware residing in read-only memory(ROM) and/or applications stored in media storage that can be read intomemory for processing by processing devices. Software can be implementedas a single program or a collection of separate programs and can bestored in non-volatile storage and copied in whole or in-part tovolatile working memory during program execution. In some embodiments,memory 220 may store data corresponding to inputs on the peripheraldevice, such as a detected movement of the peripheral device a sensor(e.g., optical sensor, accelerometer, etc.), activation of one or moreinput elements (e.g., buttons, sliders, touch-sensitive regions, etc.),or the like. Stored data may be aggregated and send via reports to ahost computing device. In some cases, the aggregated data can be used(e.g., by a receiver 400) to computer a predicted location of theperipheral device at a later time, as further described below.

Power management system 230 can be configured to manage powerdistribution, recharging, power efficiency, haptic motor power control,and the like. In some embodiments, power management system 230 caninclude a battery (not shown), a USB based recharging system for thebattery (not shown), and power management devices (e.g., voltageregulators—not shown). In certain embodiments, the functions provided bypower management system 230 may be incorporated into processor(s) 210.The power source can be a replaceable battery, a rechargeable energystorage device (e.g., super capacitor, Lithium Polymer Battery, NiMH,NiCd), or a corded power supply. The recharging system can be anadditional cable (specific for the recharging purpose) or it can use aUSB connection to recharge the battery.

Communication system 240 can be configured to provide wirelesscommunication with a corresponding host computing device (e.g., 105,110, 115), or other devices and/or peripherals, according to certainembodiments. Communication system 240 can be configured to provideradio-frequency (RF), Bluetooth®, Logitech proprietary communicationprotocol (e.g., Unifying, Gaming Light Speed, or others), infra-red(IR), ZigBee®, Z-Wave, or other suitable communication technology tocommunicate with other computing devices and/or peripheral devices.System 200 may optionally comprise a hardwired connection to thecorresponding host computing device. For example, input device 130 canbe configured to receive a Universal Serial Bus (USB) cable to enablebi-directional electronic communication with the corresponding hostcomputing device or other external devices. Some embodiments may utilizedifferent types of cables or connection protocol standards to establishhardwired communication with other entities. In some aspects,communication ports (e.g., USB), power ports, etc., may be considered aspart of other blocks described herein (e.g., input detection module 150,output control modules 260, etc.). In some aspects, communication system240 can send reports generated by the processor(s) 210 (e.g., HID data,streaming or aggregated data, etc.) to a host computing device. In somecases, the reports can be generated by the processor(s) only, inconjunction with the processor(s), or other entity in system 200.Communication system 240 may incorporate one or more antennas,oscillators, etc., and may operate at any suitable frequency band (e.g.,2.4 Ghz), etc. One of ordinary skill in the art with the benefit of thisdisclosure would appreciate the many modifications, variations, andalternative embodiments thereof.

Input detection module 250 can control the detection of auser-interaction with input elements on input device 160. For instance,input detection module 250 can detect user inputs from motion sensors,keys, buttons, roller wheels, scroll wheels, touch pads, click wheels,dials, keypads, microphones, GUIs, touch-sensitive GUIs, image sensorbased detection such as gesture detection (e.g., via webcam), audiobased detection such as voice input (e.g., via microphone), or the like,as would be appreciated by one of ordinary skill in the art with thebenefit of this disclosure.

In some embodiments, output control module 260 can control variousoutputs for a corresponding peripheral input device. For instance,output control module 260 may control a number of visual output elements(e.g., mouse cursor, LEDs, LCDs), displays, audio outputs (e.g.,speakers), haptic output systems, or the like. One of ordinary skill inthe art with the benefit of this disclosure would appreciate the manymodifications, variations, and alternative embodiments thereof.

Although certain systems may not be expressly discussed, they should beconsidered as part of system 200, as would be understood by one ofordinary skill in the art. For example, system 200 may include a bussystem to transfer power and/or data to and from the different systemstherein.

It should be appreciated that system 200 is illustrative and thatvariations and modifications are possible. System 200 can have othercapabilities not specifically described herein. Further, while system200 is described with reference to particular blocks, it is to beunderstood that these blocks are defined for convenience of descriptionand are not intended to imply a particular physical arrangement ofcomponent parts. Further, the blocks need not correspond to physicallydistinct components. Blocks can be configured to perform variousoperations, e.g., by programming a processor or providing appropriatecontrol circuitry, and various blocks might or might not bereconfigurable depending on how the initial configuration is obtained.

Embodiments of the present invention can be realized in a variety ofapparatuses including electronic devices (e.g., peripheral devices)implemented using any combination of circuitry and software.Furthermore, aspects and/or portions of system 200 may be combined withor operated by other sub-systems as required by design. For example,input detection module 250 and/or memory 220 may operate withinprocessor(s) 210 instead of functioning as a separate entity. Inaddition, the inventive concepts described herein can also be applied toany peripheral device. Further, system 200 can be applied to any of theinput devices described in the embodiments herein, whether explicitly,referentially, or tacitly described (e.g., would have been known to beapplicable to a particular input device by one of ordinary skill in theart). The foregoing embodiments are not intended to be limiting andthose of ordinary skill in the art with the benefit of this disclosurewould appreciate the myriad applications and possibilities.

FIG. 3 is a simplified block diagram of a computing device 300,according to certain embodiments. Computing device 300 can implementsome or all functions, behaviors, and/or capabilities described abovethat would use electronic storage or processing, as well as otherfunctions, behaviors, or capabilities not expressly described. Computingdevice 300 includes a processing subsystem (processor(s)) 302, a storagesubsystem 306, user interfaces 314, 316, and a communication interface312. Computing device 300 can also include other components (notexplicitly shown) such as a battery, power controllers, and othercomponents operable to provide various enhanced capabilities. In variousembodiments, computing device 300 can be implemented in a host computingdevice, such as a desktop (e.g., desktop 150) or laptop computer, mobiledevice (e.g., tablet computer, smart phone, mobile phone), wearabledevice, media device, or the like, in peripheral devices (e.g.,keyboards, etc.) in certain implementations.

Processor(s) 302 can include MCU(s), micro-processors, applicationspecific integrated circuits (ASICs), digital signal processors (DSPs),digital signal processing devices (DSPDs), programmable logic devices(PLDs), field programmable gate arrays (FPGAs), processors, controllers,micro-controllers, microprocessors, or electronic units designed toperform a function or combination of methods, functions, etc., describedthroughout this disclosure.

Storage subsystem 306 can be implemented using a local storage and/orremovable storage medium, e.g., using disk, flash memory (e.g., securedigital card, universal serial bus flash drive), or any othernon-transitory storage medium, or a combination of media, and caninclude volatile and/or non-volatile storage media. Local storage caninclude a memory subsystem 308 including random access memory (RAM) 318such as dynamic RAM (DRAM), static RAM (SRAM), synchronous dynamic RAM(e.g., DDR), or battery backed up RAM or read-only memory (ROM) 320, ora file storage subsystem 310 that may include one or more code modules.In some embodiments, storage subsystem 306 can store one or moreapplications and/or operating system programs to be executed byprocessing subsystem 302, including programs to implement some or alloperations described above that would be performed using a computer. Forexample, storage subsystem 306 can store one or more code modules forimplementing one or more method steps described herein.

A firmware and/or software implementation may be implemented withmodules (e.g., procedures, functions, and so on). A machine-readablemedium tangibly embodying instructions may be used in implementingmethodologies described herein. Code modules (e.g., instructions storedin memory) may be implemented within a processor or external to theprocessor. As used herein, the term “memory” refers to a type of longterm, short term, volatile, nonvolatile, or other storage medium and isnot to be limited to any particular type of memory or number of memoriesor type of media upon which memory is stored.

Moreover, the term “storage medium” or “storage device” may representone or more memories for storing data, including read only memory (ROM),RAM, magnetic RAM, core memory, magnetic disk storage mediums, opticalstorage mediums, flash memory devices and/or other machine readablemediums for storing information. The term “machine-readable medium”includes, but is not limited to, portable or fixed storage devices,optical storage devices, wireless channels, and/or various other storagemediums capable of storing instruction(s) and/or data.

Furthermore, embodiments may be implemented by hardware, software,scripting languages, firmware, middleware, microcode, hardwaredescription languages, and/or any combination thereof. When implementedin software, firmware, middleware, scripting language, and/or microcode,program code or code segments to perform tasks may be stored in amachine readable medium such as a storage medium. A code segment (e.g.,code module) or machine-executable instruction may represent aprocedure, a function, a subprogram, a program, a routine, a subroutine,a module, a software package, a script, a class, or a combination ofinstructions, data structures, and/or program statements. A code segmentmay be coupled to another code segment or a hardware circuit by passingand/or receiving information, data, arguments, parameters, and/or memorycontents. Information, arguments, parameters, data, etc. may be passed,forwarded, or transmitted by suitable means including memory sharing,message passing, token passing, network transmission, etc. Thesedescriptions of software, firmware, storage mediums, etc., apply tosystems 200 and 300, as well as any other implementations within thewide purview of the present disclosure. In some embodiments, aspects ofthe invention (e.g., predicting a future location of a peripheraldevice) may be performed by software stored in storage subsystem 306,stored in memory 420 of receiver device 400, or both. One of ordinaryskill in the art with the benefit of this disclosure would appreciatethe many modifications, variations, and alternative embodiments thereof.

Implementation of the techniques, blocks, steps and means describedthroughout the present disclosure may be done in various ways. Forexample, these techniques, blocks, steps and means may be implemented inhardware, software, or a combination thereof. For a hardwareimplementation, the processing units may be implemented within one ormore ASICs, DSPs, DSPDs, PLDs, FPGAs, processors, controllers,micro-controllers, microprocessors, other electronic units designed toperform the functions described above, and/or a combination thereof.

Each code module may comprise sets of instructions (codes) embodied on acomputer-readable medium that directs a processor of a computing device300 to perform corresponding actions. The instructions may be configuredto run in sequential order, in parallel (such as under differentprocessing threads), or in a combination thereof. After loading a codemodule on a general purpose computer system, the general purposecomputer is transformed into a special purpose computer system.

Computer programs incorporating various features described herein (e.g.,in one or more code modules) may be encoded and stored on variouscomputer readable storage media. Computer readable media encoded withthe program code may be packaged with a compatible electronic device, orthe program code may be provided separately from electronic devices(e.g., via Internet download or as a separately packaged computerreadable storage medium). Storage subsystem 306 can also storeinformation useful for establishing network connections using thecommunication interface 312.

Computer system 300 may include user interface input devices 314elements (e.g., touch pad, touch screen, scroll wheel, click wheel,dial, button, switch, keypad, microphone, etc.), as well as userinterface output devices 316 (e.g., video screen, indicator lights,speakers, headphone jacks, virtual- or augmented-reality display, etc.),together with supporting electronics (e.g., digital to analog or analogto digital converters, signal processors, etc.). A user can operateinput devices of user interface 314 to invoke the functionality ofcomputing device 300 and can view and/or hear output from computingdevice 300 via output devices of user interface 316.

Processing subsystem 302 can be implemented as one or more processors(e.g., integrated circuits, one or more single core or multi coremicroprocessors, microcontrollers, central processing unit, graphicsprocessing unit, etc.). In operation, processing subsystem 302 cancontrol the operation of computing device 300. In some embodiments,processing subsystem 302 can execute a variety of programs in responseto program code and can maintain multiple concurrently executingprograms or processes. At a given time, some or all of a program code tobe executed can reside in processing subsystem 302 and/or in storagemedia, such as storage subsystem 304. Through programming, processingsubsystem 302 can provide various functionality for computing device300. Processing subsystem 302 can also execute other programs to controlother functions of computing device 300, including programs that may bestored in storage subsystem 304.

Communication interface (also referred to as network interface) 312 canprovide voice and/or data communication capability for computing device300. In some embodiments, communication interface 312 can include radiofrequency (RF) transceiver components for accessing wireless datanetworks (e.g., Wi-Fi network; 3G, 4G/LTE; etc.), mobile communicationtechnologies, components for short range wireless communication (e.g.,using Bluetooth communication standards, NFC, etc.), other components,or combinations of technologies. In some embodiments, communicationinterface 312 can provide wired connectivity (e.g., universal serial bus(USB), Ethernet, universal asynchronous receiver/transmitter, etc.) inaddition to, or in lieu of, a wireless interface. Communicationinterface 312 can be implemented using a combination of hardware (e.g.,driver circuits, antennas, modulators/demodulators, encoders/decoders,and other analog and/or digital signal processing circuits) and softwarecomponents. In some embodiments, communication interface 312 can supportmultiple communication channels concurrently.

User interface input devices 314 may include any suitable computerperipheral device (e.g., computer mouse, keyboard, gaming controller,remote control, stylus device, etc.), as would be appreciated by one ofordinary skill in the art with the benefit of this disclosure. Userinterface output devices 316 can include display devices (e.g., amonitor, television, projection device, etc.), audio devices (e.g.,speakers, microphones), haptic devices, etc. Note that user interfaceinput and output devices are shown to be a part of system 300 as anintegrated system. In some cases, such as in laptop computers, this maybe the case as keyboards and input elements as well as a display andoutput elements are integrated on the same host computing device. Insome cases, the input and output devices may be separate from system300, as shown in FIG. 1 . One of ordinary skill in the art with thebenefit of this disclosure would appreciate the many modifications,variations, and alternative embodiments thereof.

It will be appreciated that computing device 300 is illustrative andthat variations and modifications are possible. A host computing devicecan have various functionality not specifically described (e.g., voicecommunication via cellular telephone networks) and can includecomponents appropriate to such functionality. While the computing device300 is described with reference to particular blocks, it is to beunderstood that these blocks are defined for convenience of descriptionand are not intended to imply a particular physical arrangement ofcomponent parts. For example, processing subsystem 302, storagesubsystem 306, user interfaces 314, 316, and communications interface312 can be in one device or distributed among multiple devices. Further,the blocks need not correspond to physically distinct components. Blockscan be configured to perform various operations, e.g., by programming aprocessor or providing appropriate control circuitry, and various blocksmight or might not be reconfigurable depending on how an initialconfiguration is obtained. Embodiments of the present invention can berealized in a variety of apparatus including electronic devicesimplemented using a combination of circuitry and software. Hostcomputing devices or even peripheral devices described herein can beimplemented using system 300.

FIG. 4A shows aspects of a receiver system configured to communicativelyand bi-directionally couple one or more peripheral devices to a hostcomputing device, according to certain embodiments. Receiver system 400is electrically and communicatively coupled to a host computing device402 (e.g., laptop computer) and configured to wirelessly receive datafrom one or more peripheral devices, which may include a computer mouse404, headset 406, speakers 408, or the like. Receiver system 400 isshown as a dongle that is physically coupled to a USB socket on hostcomputing device 402, although other implementations are possible (e.g.,a wireless receiver integrated with the motherboard of host computingdevice 402).

FIG. 4B shows a system level functional block diagram representation ofhow receiver 400 may operate with respect to a host computing device andone or more peripheral devices, according to certain embodiments. Insome embodiments, system 200 (described above with respect to FIG. 2 )may correspond to a peripheral device (e.g., compute mouse 404), system300 (described above with respect to FIG. 3 ) may correspond to a hostcomputing device (e.g., laptop 402), and system 400 may correspond tothe receiver dongle shown in FIG. 4A. Receiver 400 can include one ormore processors 410, memory block 420, host interface 430, powermanagement block 440, communication system 450, oscillator 452, andantenna 454. Other components (e.g., system bus) may be included but notshown, as would be appreciated by one of ordinary skill in the art withthe benefit of this disclosure.

Processor(s) 410 may include one or more microprocessors and can beconfigured to control the operation of system 400. Alternatively,processor(s) 210 may include one or more MCUs, DSPs, ASICs, DSPDs, PLDs,FPGAs, or other electronic units designed to perform a function orcombination of methods, functions, etc., described throughout thisdisclosure (e.g., performing some or all aspects of methods 600-900)either of which may include supporting hardware and/or firmware (e.g.,memory, programmable I/Os, etc.), as would be appreciated by one ofordinary skill in the art. Processor 410 may work in conjunction withprocessors 210 or 302 to perform some or all of said predictivecomputations (e.g., see methods 600-900) described throughout thisdisclosure. One of ordinary skill in the art would understand the manyvariations, modifications, and alternative embodiments that arepossible.

Memory block 420 can store one or more software programs to be executedby processors (e.g., processor(s) 410). It should be understood that“software” can refer to sequences of instructions that, when executed byprocessing unit(s) (e.g., processors, processing devices, etc.), causesystem 400 to perform certain operations of software programs. Theinstructions can be stored as firmware residing in read-only memory(ROM) and/or applications stored in media storage that can be read intomemory for processing by processing devices. Software can be implementedas a single program or a collection of separate programs and can bestored in non-volatile storage and copied in whole or in-part tovolatile working memory during program execution. In some aspects,memory 420 may include instructions to perform the various aspects oflocation prediction further described below with respect to FIGS. 5-9 .

A firmware and/or software implementation within system 400 or inconjunction with other memory resources external to system 400 may beimplemented with modules (e.g., procedures, functions, and so on). Amachine-readable medium tangibly embodying instructions may be used inimplementing methodologies described herein. Code modules (e.g.,instructions stored in memory) may be implemented within a processor orexternal to the processor.

Power management system 440 can be configured to manage powerdistribution throughout receiver 400, manage power efficiency, and thelike, and can be functionally similar to block 230 of FIG. 2 . In someembodiments, power management system 230 can distribute power receivedvia USB from host computing device 402

Communication system 450 can be configured to manage wirelesscommunication with one or more peripheral devices 404, 406, 408, otherhost computing devices, or any suitable electronic device configured tocommunication with host computing device 402, according to certainembodiments. Communication system 450 can be configured to provideradio-frequency (RF), Bluetooth® and/or variants (e.g., BLE), Logitechcommunication protocol (e.g., Unifying, Gaming LightSpeed, or others),infra-red (IR), ZigBee®, Z-Wave, or other suitable communicationtechnology to communicate with other computing devices and/or peripheraldevices. In some aspects, communication system 450 can receive reportsgenerated by the one or more peripheral devices (e.g., HID data,streaming or aggregated HID data, media data, etc.). Communicationsystem 450 may incorporate one or more antennas 454, oscillators 452,etc., and may operate at any suitable frequency band (e.g., 2.4 Ghz),etc., as would be appreciated by one of ordinary skill in the art withthe benefit of this disclosure.

Host interface 430 may communicate with system 300 (e.g., host computingdevice 402) via a USB communication protocol or other suitable interface(e.g., serial/parallel port, etc.), according to certain embodiments, aswould be appreciated by one of ordinary skill in the art with thebenefit of this disclosure.

Many of the embodiments described herein perform aspects of predictivelocation at receiver 400 using, for instance, processor(s) 410 andmemory 420, as described above. However, some embodiments may performthe novel methods described herein at the peripheral device or hostcomputing device, as would be appreciated by one of ordinary skill inthe art with the benefit of this disclosure.

As described above, report rates (e.g., with HID reports) as fast as 1ms or faster can be expected in high performance peripheral devices,which can be challenging to maintain when multiple peripheral devices(e.g., gaming grade computer mouse, keyboard, headset, speakers, etc.)are wirelessly coupled to and simultaneously sending data to the hostdevice. Maintaining a fast report rate can pose a significant power drawfor peripheral device. For example, the implementation of wirelesscommunication for peripheral devices (e.g., computer mice) may typicallybe designed to account for approximately 10% of a total power draw froma local battery (e.g., power management system 230). High performancedevices with fast report rates may account for up to 40% of the totalpower budget, which can markedly reduce battery life in these devices.

FIG. 5 is a graph 500 showing a relationship between a report rate and amaximum battery life for a peripheral device, according to certainembodiments. Graph 500 shows an inverse non-linear relationship betweenincreasing report rates and total battery life, which illustrates thechallenges presented with faster and faster report rates. For instance,for peripheral devices generating reports at 8 ms intervals, a typicalbattery life may be approximately 74 hours. At 4 ms, battery life maydrop to approximately 68 hours. Battery life continues to drop atnon-linear rates at 2 ms report rates (e.g., 60 hour battery life) and 1ms report rates (e.g., 49 hour battery life).

Using the predictive techniques described herein, peripheral devices maysubmit reports (e.g., including aggregated sensor data) at slower reportrates (e.g., 8 ms), which can help maintain better power dissipationcharacteristics, and gain the benefit of faster report rates (e.g., 1ms) by allowing a downstream device (e.g., receiver 400 and/or hostcomputing device 402) to compute and predict a position of theperipheral device at a later time (typically at a faster rate than theslower report rate) based on aggregated velocity and acceleration datain each received report from the peripheral device, and generate newreports using the predicted positions at the desired faster reportrates. This is made possible because predictions can be made (e.g., atreceiver 400) at a much faster rate than the rate that the peripheraldevice sends reports, and during those interim periods between reports,location predictions can be used to generate additional reports toachieve the faster report rate. In such cases, estimations can beupdated and/or corrected when the next report from the peripheral devicecomes (e.g., 8 ms later), which can include data for the peripheraldevices next current position. Thus, faster report rates at the receiver(e.g., using actual and predictive location data) can be generated basedon peripheral device reports sent at slower report rates with thebenefit of lower power dissipation requirements and improved batterylife on the corresponding peripheral device. In some estimations, abattery life gain of a factor of 2 or more can be possible by changingthe report rate and, in some cases, a polling rate of one or moresensors on a peripheral device, although other performance outcomes arepossible.

There are myriad ways of implementing the predictive location techniquesdescribed herein. For example, in some embodiments, the peripheraldevice may always transmit data (e.g., reports) at a fixed report rateand the receiver and/or host computing device may generate the fasterreport rate based on the predictive techniques described above. In somecases, a user may change a mode of operation on the peripheral device(e.g., via a button) and/or software running on the host computingdevice (e.g., via a graphical user interface (GUI)) to switch betweendifferent reporting speeds. In such cases, either the peripheral devicecan change its report rate accordingly, or the receiver/host computingdevice can change its prediction-based report rate to a desired setting.One of ordinary skill in the art with the benefit of this disclosurewould appreciate the many modifications, variations, and alternativeembodiments thereof.

FIG. 6 is a swim lane diagram showing aspects of a method 600 ofpredicting a displacement of a peripheral device to achieve an increasedreport rate and lower power dissipation, according to certainembodiments. Method 600 can be performed by processing logic that maycomprise hardware (circuitry, dedicated logic, etc.), software operatingon appropriate hardware (such as a general purpose computing system or adedicated machine), firmware (embedded software), or any combinationthereof. For instance, aspects of method 600 performed by computerperipheral device 404, receiver 400, and host computing device 402 maybe performed by processors 210, 410, and 302 respectively. In theembodiment that follows, a peripheral device generates and sends areport at a report rate of 3 ms. The report typically includes anaggregated batch of sensor data, which may include displacement data(e.g., from an optical sensor, accelerometer, Hall effect sensor, etc.)corresponding to a physical movement of the peripheral device inphysical space (e.g., 2D movement along an underlying surface, in-air 3Dmovement, etc.), movement of a control element (e.g., a scroll wheel ona mouse) or other type of movement, as would be appreciated by one ofordinary skill in the art with the benefit of this disclosure. Areceiver then receives the report and during the period (e.g., 3 ms)between subsequent reports from the peripheral device, the receiver canuse the sensor data to determine a present trajectory and predict afuture position or positions of the peripheral data at later intervals(typically faster than the peripheral device report rate) until the nextreport comes and the receiver can get the next actual position from thenext batch of aggregated sensor data and correct any inaccuracies in thecurrent predicted location of the peripheral device. In the embodimentof FIG. 6 , the receiver intercepts the report and generates a newreport based on a predicted position approximately 1 ms later and sendsthe new report to the host computing device. The receiver can do thisrepeatedly to effectively provide a report to the host computing deviceat a 1 ms report rate and may correct any error in the actual locationof the peripheral device when the receiver gets the next report (e.g.,every 3 ms) from the peripheral device. Note that the particularembodiments presented in FIG. 6 shows how a 3 ms report rate can beeffectively increased to a 1 ms report rate by way of the predictivecalculations described above, however other rates are possible. Forinstance, some embodiments may have 8 ms report rates from theperipheral device and 1 ms report rates from the receiver. Note thatgreater time differentials between the receiver's actual versuspredicted location reporting may be proportional to an amount of error,such that an 8 ms to 1 ms increase via the predictive techniquesdescribed above may yield markedly improved power dissipationcharacteristics, however the error in the predicted vs. actual locationmay be greater than other embodiments with a 3 ms to 1 ms increase, aswould be appreciated by one of ordinary skill in the art with thebenefit of this disclosure.

Referring back to FIG. 6 , at operation 610, method 600 begins withcomputer peripheral device (CPD) 404 receiving sensor data, aggregatingthe sensor data, generating an initial report (A1) based on theaggregated sensor data, and submitting the report A1 to a receiver 400via a wireless communications protocol (e.g., Bluetooth® or variants,Logitech Unifying, Logitech LightSpeed, IR, NFC, RF, or the like).Sensor data can be received at any suitable rate, typically ranging from1 ms (1 kHz—a relatively slow rate) to 58 μs (17 kHz—a relatively fastrate), however faster report rates are possible, which can be preferablefor computing velocity and acceleration trends. Typically, amicrocontroller (e.g., processor 210) in the input device (e.g.,computer mouse 130) reads the displacement in an optical sensor (orother suitable movement tracking sensor) at an interval defined by theinput device firmware, as would be appreciated by one of ordinary skillin the art with the benefit of this disclosure. Typical sensor may beoptical sensors (e.g., for measuring movement of a computer mouse alongan underlying surface), an inertial measurement unit and/or externaltracking systems, such as lighthouses for tracking movement in 3D space,magnetic sensors (e.g., Hall sensors), and resistive sensors (e.g.,potentiometers) for measuring scroll wheel rotation.

-   At operation 612, receiver 400 receives the report A1 from CPD 404,    analyzes the aggregated sensor data, determines a present location    of CPD 404 and/or computes a trajectory of CPD 404 (e.g., based on    velocity and acceleration data of CPD 404), predicts a future    displacement of CPD 404, generates a substitute report B1 (e.g.,    with a current location of CPD 404 and a predicted location(s) of    CPD 404 at future time intervals), and sends report B1 to a host    computer device (HCD) 402. As shown in FIG. 4A, receiver 400 may be    physically coupled to HCD 402 via USB port or other suitable    hardwired or wireless communication protocol. At operation 613, HCD    402 receives report B1 and uses data therein to control an element    (e.g., a cursor) on a display. Note that cursor control is but one    embodiment and reports from CPD 404 can be configured to control any    suitable aspect of HCD 402, as would be appreciated by one of    ordinary skill in the art with the benefit of this disclosure.

At operation 614, receiver 400 may generate a second substitute reportB2 based on the predicted future displacement of CPD 404 performed atoperation 612. At operation 615, HCD 402 receives report B2 and controlsthe element on the display therewith. At operation 616, receiver 400 maygenerate a third substitute report B3 also based on the computed futuredisplacement of CPD 404 performed at operation 612, i.e., the latestreport from CPD 404. At operation 617, HCD 402 receives report B3 anduses data therein to control the element on the display. In FIG. 6 ,receiver 400 is shown to submit reports to HCD 402 at 1 ms intervals,while CPD 404 submits reports to receiver 400 at 3 ms intervals.Receiver 400 can submit report B2 to HCD 402 at any suitable interval.Typically, displacement computations and trajectory predictions can beperformed at substantially faster rates than report rates (e.g., greaterthan one order of magnitude), and can often be performed at sub-microsecond rates (e.g., 50-500 μs). As such, substitute reports that includetrajectory calculations and predicted displacement may be sent at reportrates that are faster than the reports received from CPD 404. In orderto maintain an operating specification of a 1 ms report rate, receiver400 uses the predicted displacement data to generate and send substitutereports at that rate, however faster (e.g., 500 μs) or slower (e.g., 2ms) report rates are possible. One of ordinary skill in the art with thebenefit of this disclosure would appreciate the many modifications,variations, and alternative embodiments thereof.

At operation 620, method 600 continues with CPD 404 receiving new sensordata e.g., aggregating the new sensor data, generating a next report(A2) based on the aggregated sensor data, and submitting the report A2to receiver 400. At operation 622, receiver 400 receives report A2 fromCPD 404, analyzes the aggregated sensor data, calculates a currenttrajectory of CPD 404 based on the aggregated sensor data, predicts anupdated future displacement of CPD 404, generates a substitute reportB4, and sends report B4 to HCD 402. At operation 623, HCD 402 receivessubstitute report B4. Note that receiver 400 may correct or improve anerror introduced in a previously predicted displacement of CPD 404(e.g., determined at operation 616) using each new set of aggregatedsensor data (i.e., the actual movement data) being received at 3 msintervals (e.g., operations 610, 620, 630). At operation 624, receiver400 may generate a substitute report B5 based on the predicted futuredisplacement of CPD 404 performed at operation 622. At operation 625 HCD402 receives report B5 and controls the element on the displaytherewith. At operation 626, receiver 400 may generate a substitutereport B6 based on the predicted future displacement of CPD 404performed at operation 622. At operation 627 HCD 402 receives report B6and controls the element on the display therewith. The method maycontinue such that CPD 404 continues to generate and send reports at a 3ms report rate, and receiver 400 may continue to intercept said reports,and send new reports to HCD 402 at 1 ms intervals that can include anactual and predicted trajectory of CPD 404, thereby achieving a 1 msreport rate from a peripheral device reporting at 3 ms intervals.

FIG. 7 is a simplified flow chart showing aspects of a method 700 forpredicting a displacement of a peripheral device to achieve an increasedreport rate and lower power dissipation, according to certainembodiments. Method 700 can be performed by processing logic that maycomprise hardware (circuitry, dedicated logic, etc.), software operatingon appropriate hardware (such as a general purpose computing system or adedicated machine), firmware (embedded software), or any combinationthereof. For instance, aspects of method 700 can be performed byprocessor 210 of CPD 404 (e.g., operations 710-730) and processor 410 ofreceiver 400 (e.g., operations 740-790). In some embodiments, operations740-790 may be performed by a host computing device, one or morenetworked computing devices, computing resources on the cloud, or thelike, as would be appreciated by one of ordinary skill in the art withthe benefit of this disclosure.

At operation 710, method 700 can include controlling, by one or moreprocessors, a motion sensor (e.g., optical sensor) disposed on acomputer peripheral device, the motion sensor configured to generatemovement data at intervals, the movement data corresponding to a trackedmovement of the computer peripheral device with respect to an underlyingsurface, according to certain embodiments. Alternatively oradditionally, other sensors can be controlled on any suitable computerperipheral device to generate corresponding sensor data. The computerperipheral device can be a computer mouse, virtual/augmented realitycontroller, remote control, game controller, or the like. Sensor datamay correspond to a displacement of the computer input device along anunderlying surface (e.g., as presented in the non-limiting embodiment ofFIG. 7 ), movement in 3D space, or the like. Sensor data can come fromany suitable type of sensor including an optical sensor (e.g., to detect2D movement along a surface), Hall-type sensor (e.g., to detect arotation of a scroll wheel using magnetic fields), inertial measurementunit (e.g., to detect acceleration, movement in 3D space),accelerometer, gyroscope, or the like. Although the embodiment of FIG. 7relates closely to a computer mouse and corresponding sensors, one ofordinary skill in the art with the benefit of this disclosure wouldappreciate the many options, modifications, variations, and alternativeuses with respect to sensor types and computer peripheral devices.

At operation 720, method 700 can include aggregating, by the one or moreprocessors, a plurality of intervals of movement data (e.g., from theoptical sensor data) into a first report, according to certainembodiments. Alternatively or additionally, acceleration data from oneor more additional sensors (e.g., IMU) may be included and aggregated inthe first report. Each interval of the plurality of intervals maycorrespond to each set of sensor data generated by the sensor and sentto the one or more processors. For example, an optical sensor may sendmultiple sets of movement data to the one or more processors over aperiod of time, where the time between each set of movement data that issent may correspond to an interval.

At operation 730, method 700 can include sending, by the one or moreprocessors, the first report to a receiver coupled to a host computingdevice, according to certain embodiments.

In some aspects, the first report can be sent via a wirelesscommunications protocol at a first report rate (e.g., 3-8 ms) that isslower than a rate of each interval of generated movement data (e.g., <1ms, in some cases less than 100 μs.

At operation 740, method 700 can include receiving the first report bythe receiver coupled to the host computing device, according to certainembodiments. Typically, the first report is intercepted and laterreplaced with a second report (see, e.g., operation 780) as furtherdescribed below. However some embodiments may pass the first report tothe host computing device (e.g., every 8 ms) along with a second report(e.g., every 1 ms) to provide predictions for future displacement forthe computer peripheral device, and software on the host computingdevice may use both reports to determine a position of the computerperipheral device. One of ordinary skill in the art with the benefit ofthis disclosure would appreciate the many modifications, variations, andalternative embodiments thereof.

At operation 750, method 700 can include computing, by the receiver, acurrent velocity and acceleration of the computer peripheral devicebased on the plurality of intervals of movement data in the firstreport, according to certain embodiments. The plurality of intervals ofmovement data can include a succession of data showing a movement of thecomputer peripheral device, which can be analyzed and integrated forinstance to determine the velocity and/or acceleration from positiondata. In some cases, movement data may include IMU data, as noted above,which can provide acceleration data that may be used todetermine/supplement velocity and acceleration calculations. In someembodiments, only the velocity may be computed and it may be assumedthat the acceleration is zero during a short time interval. Thus, in thevarious embodiments described herein, it can be assumed that computingthe acceleration may be an optional step.

At operation 760, method 700 can include predicting, by the receiver, atrajectory of the computer peripheral device based on the first report,according to certain embodiments. As described above, the first reportmay include an aggregated set of data including movement data over aperiod of time. The prediction of the computer peripheral device'strajectory, including future positions, velocity, and acceleration canbe made with varying levels of accuracy depending on the amount of databeing used for the prediction, and how far out the prediction is beingmade (e.g., 1 ms, 2 ms, 5 ms, etc.). For example, calculations usingrelatively large amounts of movement data (e.g., 10+ data points) topredict a future position relatively close in time (e.g., 1 ms) arelikely to be more accurate than calculations using relatively smallamounts of movement data (e.g., <3 data points), as would be appreciatedby one of ordinary skill in the art with the benefit of this disclosure.In the example of FIG. 6 , movement predictions are made 2 ms out beforethe next set of movement data is received, including the next set ofactual movement data (e.g., based on sensor data), which can be used tocorrect prediction errors in the intervening calculations betweenreports from the computer peripheral device. In certain embodiments, astate estimator (e.g., operating on the receiver) can be used forpredicting the trajectory of the computer peripheral device. The stateestimator may be a linear state estimator, may include a Kalman filter(KF), or an extended Kalman filter (EKF) configured to incorporate anestimated error to balance error correction of the computed currentvelocity and acceleration of the computer peripheral device, or othermethod of estimation, as would be appreciated by one of ordinary skillin the art with the benefit of this disclosure. Typically, between 1 and5 intervals of movement data may be aggregated, with one intervaltypically being used for basic velocity estimations, two intervals forimproved velocity and basic acceleration estimations, and more forfurther improved velocity and acceleration estimations. In someembodiments, movement data may be encoding a displacement during aninterval of time (e.g., a velocity), such that sensors may reportvelocity rather than position.

In some aspects, machine learning can be used to supplement thepredictions by incorporating learned patterns of movement behavior(e.g., in clerical or gaming applications). More specifically, machinelearning (ML) can be used to compute the error correction for a specificuser, for example. Similar to graphology for signatures, a user's handcontrolling mouse movement will be specific to the user in a particularbiometric manner. Machine learning can be trained to minimize thetrajectory error by applying a correction factor to the computedpredictive trajectory. Alternatively or additionally, ML can be trainedto predict a future trajectory and motion intervals for specific users.Since the optical sensor and the mouse can send the ground truthtrajectory regularly at a lower report rate, ML can be used to minimizethe error of the predicted trajectory after a certain time of training.Machine learning would typically run on either the receiver 400 or hostcomputer 402. By way of example, for a certain period of time, an MLalgorithm could be configured in training mode where the sensor data issent at a high report rate to the receiver and the receiver uses thisdata to compute the ground truth trajectory. In parallel, the receivercan aggregate a plurality of reports to compute a simulated lower reportrate data, then the receiver can use this lower report rate data as aninput of the predictive algorithm. The receiver can then compute theerror between the simulated predictive trajectory and the ground truthtrajectory. Then, the receiver can train the machine learning algorithmto minimize this trajectory error. After a certain period of time, theML algorithm may exit the training mode, and can use the trained errorcorrection to further improve the predictive algorithm.

At operation 770, method 700 can include computing, by the receiver, afuture incremental displacement of the computer peripheral device basedon the predicted trajectory, according to certain embodiments. Theincremental displacement may be made at any succession of intervals(e.g., 1 ms, 2 ms, 3 ms, 5 ms, etc.) over any period of time (e.g., 5 msout). In some aspects, the incremental displacement and/or the period oftime may be dictated, at least in part, by the frequency at which newactual position data (e.g., sensor data) is received (included in eachreport from the computer peripheral device), which can be used to updateand correct a current predicted location, velocity, and/or accelerationof the computer peripheral device.

At operation 780, method 700 can include generating, by the receiver, asecond report that includes the computed current velocity (andoptionally the acceleration) of the computer peripheral device and thecomputed future incremental displacement of the computer peripheraldevice, according to certain embodiments. The second report replaces thefirst report, which is typically intercepted by the receiver, howeversome embodiments may still pass the first report to the host computingdevice in the manner described above with respect to operation 740.

At operation 790, method 700 can include sending, by the receiver to thehost computing device, according to certain embodiments. In some cases,the second report rate (e.g., typically 1-2 ms intervals or faster) canbe faster than the first report rate (e.g., typically 2-10 ms intervalsor slower).

It should be appreciated that the specific steps illustrated in FIG. 7provide a particular method 700 for predicting a displacement of aperipheral device to achieve an increased report rate and lower powerdissipation, according to certain embodiments. Other sequences of stepsmay also be performed according to alternative embodiments. Furthermore,additional steps may be added or removed depending on the particularapplications. For example, although a single report interception (e.g.,first report) and generation (e.g., second report) is described in thepresent method, it would be understood by those of ordinary skill in theart with the benefit of this disclosure that the predictive reportingoccurs repeatedly at any suitable rate or duration. For example, themethod may further include receiving, by the receiver coupled to thehost computing device, a third report including an aggregation of a nextplurality of intervals of movement data, the third report received afterthe first and second reports; updating the computed current velocity(and optionally the acceleration) of the computer peripheral devicebased on the third report; predicting the trajectory of the computerperipheral device based on the third report; and sending, by thereceiver to the host computing device, the third report at the secondreport rate. In some embodiments, method 700 can further includecontrolling, by the one or more processors, an IMU on the computerperipheral device, the IMU configured to generate acceleration datacorresponding to the tracked movement of the computer peripheral devicewith respect to the underlying surface. As described above, any suitableperipheral computing device may be used, however in many embodiment theperipheral computing device may be a computer mouse, and the receivermay be a USB dongle physically and communicatively coupled to the hostcomputing device. In some aspects, the second report rate can have alower power requirement than the first report rate, as further describedabove at least with respect to FIG. 5 . Any combination of changes canbe used and one of ordinary skill in the art with the benefit of thisdisclosure would understand the many variations, modifications, andalternative embodiments thereof.

In the previous embodiments, the predictive techniques described hereinwere presented as a way to improve power characteristics of a computerperipheral device and to potentially and markedly enhance battery life,which can be an important feature in today's competitive market.However, in some implementations, movement prediction may be used tosupplement and, in some cases, replace reports to deal with deleteriouseffects often encountered in wireless communications includinginterference, jitter, or the like. In such cases, the predictivetechniques described herein may be used to maintain a particular reportrate (e.g., 1 ms report rate) by sending a generated report with apredicted movement of the computer peripheral device when a report isnot available (e.g., lost or corrupted packets) to help prevent thereport rate from dropping below a target rate. For example, a computerperipheral device may generate report rates at 1 ms, which includesactual movement data from the computer input device. In some aspects,the predictive techniques described herein may be employed only when thereport is missing to ensure a consistent delivery at the host computingdevice at the 1 ms specification, as further describe below with respectto FIG. 8 . In such cases, the next report with actual position data canbe used to correct the predicted location (e.g., with a state estimatorto reduce error). In some embodiments, a motivation to employ thepredictive techniques described herein may be to simply increase theeffective report rate when battery life is not a concern (e.g., wirelessdevices with a continuous/periodic power source, such as a from apowered mouse pad).

Kalman filtering, also known as linear quadratic estimation, can includethe use of a series of measurements observed over time, containingstatistical noise and other inaccuracies, and produces estimates ofunknown variables that tend to be more accurate than those based on asingle measurement alone, by estimating a joint probability distributionover the variables for each timeframe. Kalman filtering typically worksas a two-step process. In a prediction step, the Kalman filter producesestimates of the current state variables, along with theiruncertainties. Once the outcome of the next measurement (typicallycorrupted with some amount of error, including random noise) isobserved, these estimates are updated using a weighted average, withmore weight being given to estimates with higher certainty. Kalmanfiltering may be recursive. It can run in real time, using the presentinput measurements and the previously calculated state and itsuncertainty matrix and no additional past information may be necessary.Typically, using a Kalman filter assumes that the errors are Gaussian.

FIG. 8 is a simplified flow chart showing aspects of a method 800 forpredicting a displacement of a peripheral device to achieve a targetreport rate, according to certain embodiments. Method 800 can beperformed by processing logic that may comprise hardware (circuitry,dedicated logic, etc.), software operating on appropriate hardware (suchas a general purpose computing system or a dedicated machine), firmware(embedded software), or any combination thereof. In certain embodiments,method 800 can be performed by receiver 400. Alternatively oradditionally, method 800 may be performed by aspects of system 200, 300,400, or a combination thereof. In summary, method 800 may be performedby a receiver, a host computing device, one or more networked computingdevices, computing resources on the cloud, or the like, as would beappreciated by one of ordinary skill in the art with the benefit of thisdisclosure.

At operation 810, method 800 can include receiving n reports at expectedintervals, according to certain embodiments. For instance, a receiver400 may expect to receive a succession of reports from a computerperipheral device 404 at 1 ms intervals. In some aspects, the reportsare intercepted and later replaced with new reports (see, e.g.,operation 860). However, certain embodiments may send both to thedestination device (e.g., host computing device 402). One of ordinaryskill in the art with the benefit of this disclosure would appreciatethe many modifications, variations, and alternative embodiments thereof.

At operation 820, method 800 can include determining whether a nextconsecutive (n+1) report has been received during the expected interval,according to certain embodiments.

At operation 830, if the (n+1) is received within a designated window(e.g., within a 1 ms interval), method 800 may proceed with determiningwhether the (n+1) report is corrupted, according to certain embodiments.For instance, some reports may contain corrupted packets (e.g.,containing indecipherable or unusable data).

At operation 840, if the (n+1) report is received at the expectedinterval and is not corrupted, method 800 can include generating a newreport based on the sensor based on sensor data, according to certainembodiments. That is, because the report is received on time and isintact, the actual position data (e.g., based on the present and mostup-to-date aggregated sensor data) from the computer peripheral devicecan be used to generate a report.

At operation 860, method 800 can include submitting the newly generatedreport to the host computing device, according to certain embodiments.In some embodiments, the new report may replace the intercepted (n+1)report received at operation 810. In further embodiments, the new reportmay supplement the (n+1) report.

At operation 820, if the (n+1) report is not received at the expectedinterval, method 800 can include generating a new (n+1) report based onpredictive data, according to certain embodiments (operation 850). Asdescribed above, a predicted position, velocity, and/or acceleration ofthe computer peripheral device can be calculated based on the aggregatedsensor data. Thus, a new report (n+1) with predicted movement data forthe computer peripheral device will be sent in place of the missing(n+1) report. The new report is then submitted to the host computingdevice (operation 860).

At operation 830, if the (n+1) report is corrupted, method 800 caninclude generating a new (n+1) report based on predictive data,according to certain embodiments (operation 850). Thus, a new report(n+1) with predicted movement data for the computer peripheral devicewill be sent in place of the corrupted (n+1) report. The new report isthen submitted to the host computing device (operation 860).

Note that each path can still yield a target report rate regardless ofthe state of the (n+1) report. For example, path a after operation 810generates a new report based on the present up-to-date movement data ata 1 ms report rate. Paths b and c, despite the inclusion ofprediction-based movement data, can still submit the new report to thehost computing device at the 1 ms report rate target. Note that thevarious embodiment described herein refer primarily to reports includingmovement data. It would be understood by those of ordinary skill in theart that reports can also include other types of data (e.g., HIDcommands, etc.) and in no way limits the possibilities andimplementations of the predictive techniques described herein.

It should be appreciated that the specific steps illustrated in FIG. 8provide a particular method 800 for predicting a displacement of aperipheral device to achieve a target report rate, according to certainembodiments. Other sequences of steps may also be performed according toalternative embodiments. Furthermore, additional steps may be added orremoved depending on the particular applications. Any combination ofchanges can be used and one of ordinary skill in the art with thebenefit of this disclosure would understand the many variations,modifications, and alternative embodiments thereof.

In some embodiments, a movement (trajectory) prediction may depend onone or multiple reports previously received (e.g., velocity data,acceleration data, change of angle data, etc.) sent by the device asexplained above with respect to method 700. Typically, velocity data maybe first degree data (e.g., highest priority), followed by accelerationdata (e.g., second degree data—lower priority than velocity data), thenchange-of-angle data (third degree data—lower in priority than velocityand acceleration data). The movement prediction may be based on multipletype of movement data (e.g., degrees 1-3), including data going back forlonger periods of time. However, predictions based on periods of timegreater than 8 ms may be noticeable to discerning users. A simplepredictive method based on the latest report received may be predictedusing a basic formula:

$\sum\limits_{1}^{n}{1/\left( \frac{x}{2n} \right)}$Thus, in cases where one report is missing, the added values may becalculated by using 1/(x/2), as represented by graph 1100 of FIG. 11 ,which shows a number of reports versus time for three scenariosincluding when there is a missing report, an expected report rate (e.g.,based on the previous rate of reporting), and the extrapolation of thereport rate (e.g., calculated behavior). By extension, if more samplesare missing, additional reports may be represented as shown in thesecond equation below depending on the number of reports that aremissing (the second equation shows that at least three reports aremissing):

$\sum{1/\left( {\left( \frac{x}{2} \right) + \left( \frac{x}{4} \right) + \left( \frac{x}{8} \right) + {\ldots\mspace{14mu}\left( \frac{x}{2n} \right)}} \right)}$Other methods of prediction using different formulas, constants, errorcorrection, and the like, may be incorporated, as would be appreciatedby one of ordinary skill in the art with the benefit of this disclosure.

FIG. 9 is a simplified flow chart showing aspects of a method 900 forpredicting a displacement of a peripheral device to achieve a targetreport rate, according to certain embodiments. Method 900 can beperformed by processing logic that may comprise hardware (circuitry,dedicated logic, etc.), software operating on appropriate hardware (suchas a general purpose computing system or a dedicated machine), firmware(embedded software), or any combination thereof. In certain embodiments,method 900 can be performed by receiver 400. Alternatively oradditionally, method 900 may be performed by aspects of system 200, 300,400, or a combination thereof.

At operation 910, method 900 can include controlling, by one or moreprocessors, a motion sensor (e.g., optical sensor) disposed on acomputer peripheral device, according to certain embodiments. The motionsensor may be configured to generate movement data at intervals, themovement data corresponding to a tracked movement of the computerperipheral device with respect to an underlying surface.

At operation 920, method 900 can include sending, by the one or moreprocessors, a first report to a receiver coupled to a host computingdevice at a first report rate, according to certain embodiments. In someaspects, the first report can include one or more intervals of generatedmovement data, the first report being sent via a wireless communicationsprotocol,

At operation 930, method 900 can include receiving the first report bythe receiver, according to certain embodiments.

At operation 940, method 900 can include determining, by the receiver,that the first report is corrupted or received at a rate slower than thefirst report rate, according to certain embodiments.

At operation 950, method 900 can include computing, by the receiver, acurrent velocity of the computer peripheral device based on the one ormore intervals of movement data in the first report, according tocertain embodiments. In some embodiments, the acceleration is optionaland may not be computed, e.g., and may be assumed to be equal to zeroduring a short time interval (e.g., 1 ms or other suitable interval).

At operation 960, method 900 can include predicting, by the receiver, atrajectory of the computer peripheral device based on the first report,according to certain embodiments.

At operation 970, method 900 can include computing, by the receiver, afuture incremental displacement of the computer peripheral device basedon the predicted trajectory, according to certain embodiments.

At operation 980, method 900 can include generating, by the receiver, asecond report that includes the computed current velocity (andoptionally the acceleration) of the computer peripheral device and thecomputed future incremental displacement of the computer peripheraldevice, according to certain embodiments.

At operation 990, method 900 can include sending, by the receiver to thehost computing device, the second report at a second report rate that isat least as fast as the first report rate, according to certainembodiments.

It should be appreciated that the specific steps illustrated in FIG. 9provide a particular method 900 for predicting a displacement of aperipheral device to achieve a target report rate, according to certainembodiments. Other sequences of steps may also be performed according toalternative embodiments. Furthermore, additional steps may be added orremoved depending on the particular applications. Any combination ofchanges can be used and one of ordinary skill in the art with thebenefit of this disclosure would understand the many variations,modifications, and alternative embodiments thereof.

In some of the previous embodiments described above, predictive analysishas been used to improve power efficiency or to maintain a report ratewhen reports are received at inconsistent intervals or with corrupteddata. Power efficiency may be improved by allowing the computerperipheral device to operate with a lower sensor polling rate, lowerreport rate, or both continuously, conditionally (e.g., when batterylevels fall below a threshold value (e.g., 50%)), or intermittently(e.g., based on usage, further described below). Maintaining a reportrate may be important to guarantee a certain performance (e.g., 1 msreport rate) even when receiving inconsistent reports. The predictiveanalysis may be performed at a receiver (e.g., wireless dongle), a hostcomputing device, other devices/resources on a network, on the cloud viaan internet connection, or the like.

In some embodiments, predictive analysis may be used to dynamicallycontrol the operation of a computer peripheral device. Morespecifically, a report rate of a computer peripheral device may bedynamically controlled based on its usage. For example, when thecomputer peripheral device is determined to be operating relativelyslowly (e.g., below a threshold velocity, acceleration, or angle ofmovement) or it is determined that the computer peripheral device isoperating software on the host computing device that does not requirehigh performance (e.g., a word processing application), then a receiver(or host computing device, cloud computing entity, network entity, etc.)can generate and send a command to the computer peripheral devicecausing it to operate at a reduced report rate (e.g., 4-10 ms reportrate or slower, reduced sensor polling rate, or other reduction).Conversely, when the computer peripheral device is operating relativelyquickly (e.g., at or above a threshold velocity, acceleration, or angleof movement) or it is determined that the computer peripheral device isoperating software on the host computing device that does require highperformance (e.g., e-sports games), then a receiver (or host computingdevice, cloud computing entity, network entity, etc.) can generate andsend a command to the computer peripheral device causing it to operateat an increased report rate (e.g., 1 ms report rate or faster, increasedsensor polling rate, or other performance enhancement).

FIG. 10 is a simplified flow chart showing aspects of a method 1000 fordynamically controlling a report rate on a computer peripheral device,according to certain embodiments. Method 1000 can be performed byprocessing logic that may comprise hardware (circuitry, dedicated logic,etc.), software operating on appropriate hardware (such as a generalpurpose computing system or a dedicated machine), firmware (embeddedsoftware), or any combination thereof. For instance, in someembodiments, method 1000 may be performed by a host computing device,one or more networked computing devices, computing resources on thecloud, or the like, as would be appreciated by one of ordinary skill inthe art with the benefit of this disclosure. To simplify explanation,the first report rate and second report rate will be 1 ms and 4 ms,respectively. However, one of ordinary skill in the art with the benefitof this disclosure would appreciate the many modifications, variations,and alternative embodiments thereof.

At operation 1010, method 1000 can include receiving (e.g., by areceiver coupled to a host computing device) a first report from acomputer peripheral device, the first report including aggregatedmovement data detected by a motion sensor that corresponds to a trackedmovement of the computer peripheral device with respect to an underlyingsurface, according to certain embodiments. Alternatively oradditionally, other sensors can be controlled on any suitable computerperipheral device to generate corresponding sensor data. The computerperipheral device can be a computer mouse, virtual/augmented realitycontroller, remote control, game controller, or the like. Motion sensordata may correspond to a displacement of the computer input device alongan underlying surface (e.g., as presented in the non-limiting embodimentof FIG. 7 ), movement in 3D space, or the like. Sensor data can comefrom any suitable type of sensor including an optical sensor (e.g., todetect 2D movement along a surface), Hall-type sensor (e.g., to detect arotation of a scroll wheel using magnetic fields), touch sensitivesensor, resistive sensor, capacitive sensor, inertial measurement unit(e.g., to detect acceleration, movement in 3D space), accelerometer,gyroscope, or the like. Although the embodiment of FIG. 10 relatesclosely to a computer mouse and corresponding sensors, one of ordinaryskill in the art with the benefit of this disclosure would appreciatethe many options, modifications, variations, and alternative uses withrespect to sensor types and computer peripheral devices.

At operation 1020, method 1000 can include computing (e.g., by thereceiver) a current velocity of the computer peripheral device based onthe aggregated movement data in the first report. Alternatively oradditionally, an acceleration or positional movement may be computed, asdescribed above.

At operation 1030, when the current movement (e.g., velocity,acceleration, and/or angle of a change in movement) reaches a thresholdvalue method 1000 can continue to operation 1040, otherwise method 1000can continue to operation 1050, as further described below. In someembodiments, a velocity threshold may be 4 inches per second (ips)(referring to a mouse movement along an underlying surface), anacceleration threshold may be 10 m/s (1 g), a change of directionthreshold may be 10 degrees, etc. Higher or lower threshold values maybe used, multiple threshold values may be used, or the like, as would beappreciated by one of ordinary skill in the art with the benefit of thisdisclosure. Typically, if any of the measurements is higher than one ofthese thresholds, the input device may be considered to be operatingwith high activity, thereby prompting the submission of reports at thefirst report rate. When the movement is below the threshold value, thismay be indicative of a user operating the device in a manner or in acontext that does not require fast report rates, such as a user usingthe computer peripheral device with slow movements, with a softwareapplication that does not need fast report rates (e.g., a spreadsheetapplication), the user currently is inactive, or the like. The methodcan continue by using predictive movement calculations to provide reportrates by the receiver to a host computing device at a faster rate (e.g.,1 ms) than the receiver receives from the peripheral device (e.g., 4 ms)(operation 1060-1090) and cause the computer peripheral device tooperate at a reduced report rate based on its current movement data, asfurther described below.

By way of example, a typical acceleration of 5 g is common for in-gamemovements, with higher end values at or around 20 g. At suchacceleration, the maximum change of trajectory in 1 ms corresponds to 25μm to 100 μm respectively or 0.75 optical sensor pixels, respectively 3pixels. After 2 ms, this error could reach 100 μm to 400 μm respectivelyor 3 optical sensor pixels, respectively 12 pixels, which may be errorcompensated as described above.

At operation 1040, when the current movement does reach (e.g., at orabove) a threshold value, method 1000 can further include generating andsending a first command to the computer peripheral device configured tocause the computer peripheral device to send subsequent reports ofaggregated movement data at a first report rate (e.g., 1 ms reportrate). For example, movement at or above the threshold value may beindicative of a scenario where fast performance is preferred (e.g., anFPS-based video game), so the receiver (or host computer device) candynamically control the computer peripheral device to send reports morequickly. This may be achieved by causing the computer peripheral deviceto poll its corresponding movement sensors at an increased rate or justto send aggregated reports at a faster rate. One of ordinary skill inthe art with the benefit of this disclosure would appreciate the manymodifications, variations, and alternative embodiments thereof. Thefirst report (and some subsequent reports under the same conditions) canthen be sent to the host computing device at the first report rate(operation 1090).

At operation 1050, when the current movement does not reach thethreshold value, method 1000 can further include generating and sendinga second command to the computer peripheral device configured to causethe computer peripheral device to send subsequent reports of aggregatedmovement data at a second report rate (e.g., 4 ms) that is slower thanthe first report rate. For example, movement below the threshold valuemay be indicative of a scenario where fast performance is not necessary(e.g., web browsing), so the receiver (or host computer device) candynamically control the computer peripheral device to send reports moreslowly. This may be achieved by causing the computer peripheral deviceto poll its corresponding movement sensors at a reduced rate or just tosend aggregated reports at a slower rate. One of ordinary skill in theart with the benefit of this disclosure would appreciate the manymodifications, variations, and alternative embodiments thereof.

At operation 1060, method 1000 can further include predicting (e.g., bythe receiver) a trajectory of the computer peripheral device based onthe first report; computing (e.g., by the receiver) a future incrementaldisplacement of the computer peripheral device based on the predictedtrajectory (operation 1070); generating (e.g., by the receiver) a secondreport that includes the computed current velocity of the computerperipheral device and a third report that includes the computed futureincremental displacement of the computer peripheral device (operation1080); and sending (e.g., by the receiver to the host computing device)the second and third reports at the first report rate (operation 1090).In some aspects, method 1000 can further include maintaining the firstreport rate between the receiver and the host computing deviceirrespective of the report rate of the computer peripheral device. Forexample, if the report rate received from the computer peripheral deviceis fast (e.g., 1 ms), the receiver can send reports at the fast rate(e.g., 1 ms) or other suitable predetermined rate where the reportsreflect that actual movement of the computer peripheral device.Likewise, if the report rate received from the computer peripheraldevice is slow (e.g., 4 ms), the receiver still sends reports at 1 ms,but the reports may likely include predicted movement data of thecomputer peripheral device until new actual movement data is received toupdate/correct the current tracked position and movement of the computerperipheral device, as further described above.

It should be appreciated that the specific steps illustrated in FIG. 10provide a particular method 1000 for dynamically controlling a reportrate on a computer peripheral device, according to certain embodiments.Other sequences of steps may also be performed according to alternativeembodiments. Furthermore, additional steps may be added or removeddepending on the particular applications. For example, the first commandmay be configured to cause the computer peripheral device to change apolling rate for the motion sensor to a first polling rate and thesecond command can be configured to cause the computer peripheral deviceto change the polling rate for the motion sensor to a second pollingrate that is slower than the first polling rate. In another example, inresponse to determining that the computer peripheral device iscontrolling software of a first type (e.g., web browser or otherapplication that does not need high performance or fast report rates)operating on a host computing device, method 1000 can include generatingand sending the second command to the computer peripheral device. Insome cases, in response to determining that the computer peripheraldevice is controlling software of a second type (e.g., e-sports game orother application that does need high performance or fast report rates)operating on a host computing device, generating and sending the firstcommand to the computer peripheral device.

In further embodiments, method 1000 can include computing, by thereceiver, a current acceleration or motion direction of the computerperipheral device based on the aggregated movement data in the firstreport; in response to determining that the current acceleration is ator above an acceleration threshold value or the motion direction haschanged at or above an angle threshold angle, sending the first commandto the computer peripheral device; and in response to determining thatthe current acceleration is below the acceleration threshold value orthe motion direction has changed below a threshold angle, sending thesecond command to the computer peripheral device.

In certain embodiments, method 1000 can cause a computer peripheraldevice to be dynamically controlled based on a current and/or predictednear-term movement of the computer peripheral device, based on asoftware application operating on the host device that the computerperipheral device is controlling, or the like. Put simply, method 1000measures the activity of use and may cause the computer peripheraldevice to reduce a report rate when it is determined that fast movementis unlikely to occur and increase/maintain a report rate when it isdetermined that fast movement is likely. The embodiments describedherein mostly described the analysis of movement data (e.g., velocity,position movement, acceleration, etc.) to do the predictive analysis.However, other sources of data can be used, such as pixel movement on ascreen or other suitable source of data to base predictive analyses. Oneof ordinary skill in the art with the benefit of this disclosure wouldappreciate the many modifications, variations, and alternativeembodiments thereof.

There are myriad applications that the predictive analysis techniquesdescribed herein can be used. By way of example, the followingadditional methods 1200-1400 provide predictive techniques that can besimilar in scope as the other methods and systems described above (e.g.,using systems as shown in FIGS. 1-4B), and can include additionalfeatures and implementations to illustrate its wide variety of uses.

FIG. 12 is a simplified flow chart showing aspects of a method 1200 forpredicting a position of displacement of a computer peripheral devicewhen missing or corrupted reports occur, according to certainembodiments. Method 1200 can be performed by processing logic that maycomprise hardware (circuitry, dedicated logic, etc.), software operatingon appropriate hardware (such as a general purpose computing system or adedicated machine), firmware (embedded software), or any combinationthereof. In certain embodiments, method 1200 can be performed byreceiver 400. In some cases, at least some operations of method 1200 canbe performed by receiver 400 and some operations may be performed by thecomputer peripheral device. Alternatively or additionally, method 1200may be performed by aspects of system 200, 300, 400, or a combinationthereof.

At operation 1210, method 1200 can include controlling, by one or moreprocessors on a computer peripheral device, an optical sensor disposedthereon, the optical sensor configured to generate movement data atintervals, the movement data corresponding to a tracked movement of thecomputer peripheral device with respect to an underlying surface,according to certain embodiments. Alternatively or additionally, anon-board IMU can be used to generate the movement data (e.g.,acceleration data).

At operation 1220, method 1200 can include sending, by the one or moreprocessors, a first report to a receiver coupled to a host computingdevice at a first report rate, the first report including one or moreintervals of generated movement data, the first report being sent via awireless communications protocol, according to certain embodiments.

At operation 1230, method 1200 can include receiving the first report bythe receiver, according to certain embodiments.

At operation 1240, method 1200 can include determining, by the receiver,that the first report is corrupted or received at a rate slower than thefirst report rate, according to certain embodiments.

At operation 1250, method 1200 can include computing, by the receiver, acurrent velocity and acceleration of the computer peripheral devicebased on the one or more intervals of movement data in the first report,according to certain embodiments.

At operation 1260, method 1200 can include predicting, by the receiver,a trajectory of the computer peripheral device based on the firstreport, according to certain embodiments.

At operation 1270, method 1200 can include computing, by the receiver,an incremental displacement of the computer peripheral device based onthe predicted trajectory, according to certain embodiments.

At operation 1280, method 1200 can include generating, by the receiver,data indicative of a position or displacement of the computer peripheraldevice based on the computed current velocity and acceleration of thecomputer peripheral device, according to certain embodiments.

At operation 1290, method 1200 can include sending, by the receiver to ahost computing device, the data indicative of a position or displacementof the computer peripheral device at an interval that is less than twicea period of the first report rate, according to certain embodiments. Insome aspects, predicting the trajectory of the computer peripheraldevice can be performed, in part, by a state estimator operating on thereceiver, which may include a linear state estimator, or an extendedKalman filter configured to incorporate an estimated error to balanceerror correction of the computed current velocity and acceleration ofthe computer peripheral device. Typically, the first report rate is 6 msor faster. As described in various embodiments above, the first reportrate may be as fast as 1 ms or less. The second report rate is typicallyat least two times slower (e.g., first report rate=1 ms; second reportrate=2 ms, 4 ms, or other slower rate). One of ordinary skill in the artwith the benefit of this disclosure would appreciate the manymodifications, variations, and alternative embodiments thereof.

In some implementations, method 1200 can further include controlling, bythe one or more processors, an inertial measurement unit (IMU) on thecomputer peripheral device, the IMU configured to generate accelerationdata corresponding to the tracked movement of the computer peripheraldevice with respect to the underlying surface. In typicalimplementations, the peripheral computing device is a computer mouse,the receiver is a USB dongle (or other wireless transceiver) physically(or remotely) and communicatively coupled to the host computing device.

It should be appreciated that the specific steps illustrated in FIG. 12provide a particular method 1200 for predicting a position ofdisplacement of a computer peripheral device when missing or corruptedreports occur, according to certain embodiments, according to certainembodiments. Other sequences of steps may also be performed according toalternative embodiments. Furthermore, additional steps may be added orremoved depending on the particular applications. Any combination ofchanges can be used and one of ordinary skill in the art with thebenefit of this disclosure would understand the many variations,modifications, and alternative embodiments thereof.

FIG. 13 is a simplified flow chart showing aspects of a method 1300 fordynamically controlling an operation of a computer peripheral devicebased on a predicted activity level, according to certain embodiments.In certain embodiments, method 1300 can be performed by receiver 400. Incertain embodiments, at least some operations of method 1300 can beperformed by receiver 400, and some operations may be performed by thecomputer peripheral device. Alternatively or additionally, method 1300may be performed by aspects of system 200, 300, 400, or a combinationthereof.

At operation 1310, method 1300 can include sending, by a computerperipheral device, a first report to a receiver coupled to a hostcomputer device, according to certain embodiments. In some cases, thefirst report can include aggregated movement data detected by a motionsensor that corresponds to a tracked movement of the computer peripheraldevice with respect to an underlying surface. The computer peripheraldevice may be configured to transmit the first report at both a firstreport rate or a second report rate, the first report rate (e.g., 1 ms)being a higher report rate than the second report rate (e.g., 2 ms, 4ms, 8 ms, or the like).

At operation 1320, method 1300 can include receiving, by the receivercoupled to the host computing device, the first report from a computerperipheral device, according to certain embodiments.

At operation 1330, method 1300 can include computing, by the receiver, avelocity of the computer peripheral device based on the aggregatedmovement data in the first report, according to certain embodiments.

At operation 1340, method 1300 can include computing, by the receiver, atrajectory of the computer peripheral device based on the velocity ofthe computer peripheral device, according to certain embodiments.

At operation 1350, method 1300 can include determining, by the receiver,based on the trajectory of the computer peripheral device, a predictedactivity level of the computer peripheral device, according to certainembodiments. In some embodiments, the activity level of the computerperipheral device corresponds to an amount of randomness of theacceleration of the computer peripheral device over time. For example,rapid changes in acceleration may be indicative of high performance useof the peripheral device, like how a gamer might use a computer mouse inan e-sports application (e.g., FPS game). In such cases, the predictedactivity level would tend to be high. Slower changes in acceleration maycorrespond more to office use. In those cases, the predicted activitylevel would tend to be comparatively low, as would be appreciated by oneof ordinary skill in the art with the benefit of this disclosure.

In some embodiments, the activity level of the computer peripheraldevice corresponds, in part, to a location of the computer peripheraldevice on a mouse pad, and wherein the predicted activity level of thecomputer peripheral device exceeds the baseline activity level when thelocation of the computer peripheral device operates beyond a thresholddistance from a reference location (e.g., center) on the mouse pad. Forexample, a computer mouse used in an office environment may primarilyoperate in a center portion of a mouse pad. In contrast, a computermouse used in an e-sports gaming environment may move over acomparatively large surface of the mouse pad. The activity level maycorrespond this movement behavior and may incorporate other aspects suchas an amount of time within and/or outside of the threshold distance, ora number of times the peripheral device traverses the threshold in aperiod of time (e.g., 10 s), etc. One of ordinary skill in the art withthe benefit of this disclosure would appreciate the many modifications,variations, and alternative embodiments thereof.

At operation 1360, method 1300 can include comparing, by the receiver,the predicted activity level of the computer peripheral device with abaseline activity level, according to certain embodiments.

At operation 1370, method 1300 can include in response to determiningthat the predicted activity level of the computer peripheral deviceexceeds the baseline activity level, generating and sending a firstcommand to the computer peripheral device configured to cause thecomputer peripheral device to send a subsequent report of aggregatedmovement data at the first report rate, according to certainembodiments.

At operation 1380, method 1300 can include receiving, at the computerperipheral device, the first command, according to certain embodiments.

At operation 1390, method 1300 can include in response to receiving thefirst command, configuring, by the computer peripheral device, thesubsequent report of aggregated movement data to be transmitted at thefirst report rate, according to certain embodiments.

In some implementations, the computing the trajectory of the computerperipheral device can be performed by a state estimator operating on thereceiver, wherein the state estimator can be one of a linear stateestimator, a Kalman filter, or an extended Kalman filter configured toincorporate an estimated error to balance an error correction of thecomputed trajectory of the computer peripheral device.

In some embodiments, the first report can further include aggregatedacceleration data detected by an inertial measurement unit (IMU) on thecomputer peripheral device that corresponds to a tracked acceleration ofthe computer peripheral device with respect to the underlying surface.In such cases, the method can further include: computing, by thereceiver, an acceleration of the computer peripheral device based on theaggregated acceleration data in the first report and computing, by thereceiver, the trajectory of the computer peripheral device based on thecomputed velocity and acceleration of the computer peripheral device.

In some aspects, in response to determining that the predicted activitylevel of the computer peripheral device is at or below the baselineactivity level, the method can include generating and sending a secondcommand to the computer peripheral device configured to cause thecomputer peripheral device to send the subsequent report of aggregatedmovement data at a second report rate that is slower than the firstreport rate. In other words, the computer peripheral device maydynamically change from a high performance report rate to a baselinereport rate based on the predicted activity level, which may result insignificant power savings for the peripheral computer device, amongother benefits as described above.

In further embodiments, the receiver can be configured to always sendaggregated movement data to the host computing device at the firstreport rate regardless if the first or second command is sent to thecomputer peripheral device. In other words, whether the computerperipheral device is sending reports at slow or fast report rates, thereceiver may always send fast report rates. For instance, where thecomputer peripheral devices sends reports at fast report rates, thereceiver may send at the same rate. However, where the computerperipheral device sends reports at a slow report rate, the receiver mayuse predictive analysis to determine how the computer peripheral devicewould be moving based on its previous movements (e.g., velocity,acceleration, etc.) and report the predicted movement at the fast rateuntil a new report is received. In some embodiments, the peripheralcomputing device is a computer mouse, the motion sensor is an opticalsensor, and the receiver is a USB dongle physically and communicativelycoupled to the host computing device, although any of the computerperipheral device, sensor, receiver, and host computing device maydiffer, as would be appreciated by one of ordinary skill in the art withthe benefit of this disclosure.

It should be appreciated that the specific steps illustrated in FIG. 13provide a particular method 1300 for dynamically controlling anoperation of a computer peripheral device based on a predicted activitylevel, according to certain embodiments. Other sequences of steps mayalso be performed according to alternative embodiments. Furthermore,additional steps may be added or removed depending on the particularapplications. Any combination of changes can be used and one of ordinaryskill in the art with the benefit of this disclosure would understandthe many variations, modifications, and alternative embodiments thereof.

FIG. 14 is a simplified flow chart showing aspects of a method 1400 fordynamically controlling an operation of a computer peripheral devicebased on a current detected movement, according to certain embodiments.In certain embodiments, method 1400 can be performed by receiver 400. Incertain embodiments, at least some operations of method 1400 can beperformed by receiver 400, and some operations may be performed by thecomputer peripheral device. Alternatively or additionally, method 1400may be performed by aspects of system 200, 300, 400, or a combinationthereof.

At operation 1410, method 1400 can include receiving, by a receivercoupled to a host computing device, a first report from a computerperipheral device, the first report including aggregated movement datadetected by a motion sensor that corresponds to a tracked movement ofthe computer peripheral device with respect to an underlying surface,according to certain embodiments.

At operation 1420, method 1400 can include computing, by the receiver, acurrent movement of the computer peripheral device based on theaggregated movement data in the first report, according to certainembodiments.

At operation 1430, method 1400 can include, in response to determiningthat the current movement of the computer peripheral device is at orabove threshold value, generating and sending a first command to thecomputer peripheral device that is configured to cause the computerperipheral device to send subsequent reports of aggregated movement dataat a first report rate, according to certain embodiments (operation1440).

Alternatively at operation 1430, in response to determining that thecurrent movement of the computer peripheral device is below a thresholdvalue, generating and sending a second command to the computerperipheral device that is configured to cause the computer peripheraldevice to send subsequent reports of aggregated movement data at asecond report rate that is slower than the first report rate, accordingto certain embodiments (operation 1450).

In some implementations, the current movement can be a current velocity,and further in response to determining that the current velocity of thecomputer peripheral device is below a threshold value the method caninclude predicting, by the receiver, a trajectory of the computerperipheral device based on the first report; computing, by the receiver,a future incremental displacement of the computer peripheral devicebased on the predicted trajectory; generating, by the receiver, a secondreport that includes the computed current velocity of the computerperipheral device and a third report that includes the computed futureincremental displacement of the computer peripheral device; and sending,by the receiver to the host computing device, the second and thirdreports at the first report rate.

In some cases, the method can include maintaining the first report ratebetween the receiver and the host computing device irrespective of thereport rate of the computer peripheral device. For example, the receivermay send reports to the host computing device at 1 ms intervals,regardless of whether the computer peripheral device is sending reportsat a fast rate (e.g., 1 ms) or slow rate (e.g., 8 ms—where the receiverpredicts the movement and sends the prediction to the host computingdevice at the 1 ms report rate until the next report is received fromthe computer peripheral device). In some embodiments, the first commandcan be configured to cause the computer peripheral device to change apolling rate for the motion sensor to a first polling rate, and thesecond command is configured to cause the computer peripheral device tochange the polling rate for the motion sensor to a second polling ratethat is slower than the first polling rate. In certain embodiments, inresponse to determining that the computer peripheral device iscontrolling software of a first type operating on a host computingdevice, generating and sending the second command to the computerperipheral device and in response to determining that the computerperipheral device is controlling software of a second type operating ona host computing device, generating and sending the first command to thecomputer peripheral device.

In further embodiments, the method can further include computing, by thereceiver, a current acceleration or motion direction of the computerperipheral device based on the aggregated movement data in the firstreport; in response to determining that the current acceleration is ator above an acceleration threshold value or the motion direction haschanged at or above an angle threshold angle, sending the first commandto the computer peripheral device; and in response to determining thatthe current acceleration is below the acceleration threshold value orthe motion direction has changed below a threshold angle, sending thesecond command to the computer peripheral device. In someimplementations, the computer peripheral device can be a computer mouse,the receiver can be physically coupled to the host computing device, andthe receiver is wirelessly communicatively coupled to the computerperipheral device.

It should be appreciated that the specific steps illustrated in FIG. 14provide a particular method 1400 for dynamically controlling anoperation of a computer peripheral device based on a current detectedmovement, according to certain embodiments. Other sequences of steps mayalso be performed according to alternative embodiments. Furthermore,additional steps may be added or removed depending on the particularapplications. Any combination of changes can be used and one of ordinaryskill in the art with the benefit of this disclosure would understandthe many variations, modifications, and alternative embodiments thereof.

It should be noted that any of the methods (see, e.g., FIGS. 6-10 and12-14 ) and systems described herein can be performed by a receiver, bya host computing device, on the cloud at backend infrastructure, or acombination thereof, as would be appreciated by one of ordinary skill inthe art with the benefit of this disclosure. FIG. 15A shows a typicalexample of the system where the predictive analysis is performed inwhole or in large part by the receiver (e.g., a USB dongle), accordingto certain embodiments, and as further described above at least withrespect to FIGS. 4A and 4B. FIG. 15B shows a typical example of thesystem where the predictive analysis is performed in whole or in largepart by the host computing device, according to certain embodiments.FIG. 15C shows a typical example of the system where the predictiveanalysis is performed in whole or in large part by a backend system,such as on a remote server or other backend system, according to certainembodiments.

Many of the embodiments presented herein show how a movement of acomputer mouse can be predicted and reported at increased report ratesusing the techniques described above. It should be understood that thepredictive techniques can have wide-ranging applications. For instance,in addition to predicting a movement of a computer mouse over anunderlying surface (e.g., 2D movement), as shown in FIG. 16A, someembodiments may be used to predict the rotation of an input element,such as a scroll wheel, as shown in FIG. 16B or movement of a computerperipheral device (e.g., a stylus) for in-air operation over threedimensions, as shown in FIG. 16C. One of ordinary skill in the art withthe benefit of this disclosure would appreciate the many modifications,variations, and alternative embodiments thereof.

Most embodiments utilize at least one network that would be familiar tothose skilled in the art for supporting communications using any of avariety of commercially available protocols, such as TCP/IP, UDP, OSI,FTP, UPnP, NFS, CIFS, and the like. The network can be, for example, alocal area network, a wide-area network, a virtual private network, theInternet, an intranet, an extranet, a public switched telephone network,an infrared network, a wireless network, and any combination thereof.

In embodiments utilizing a network server as the operation server or thesecurity server, the network server can run any of a variety of serveror mid-tier applications, including HTTP servers, FTP servers, CGIservers, data servers, Java servers, and business application servers.The server(s) also may be capable of executing programs or scripts inresponse to requests from user devices, such as by executing one or moreapplications that may be implemented as one or more scripts or programswritten in any programming language, including but not limited to Java®,C, C# or C++, or any scripting language, such as Perl, Python or TCL, aswell as combinations thereof. The server(s) may also include databaseservers, including without limitation those commercially available fromOracle®, Microsoft®, Sybase®, and IBM®.

Such devices also can include a computer-readable storage media reader,a communications device (e.g., a modem, a network card (wireless orwired), an infrared communication device, etc.), and working memory asdescribed above. The computer-readable storage media reader can beconnected with, or configured to receive, a non-transitorycomputer-readable storage medium, representing remote, local, fixed,and/or removable storage devices as well as storage media fortemporarily and/or more permanently containing, storing, transmitting,and retrieving computer-readable information. The system and variousdevices also typically will include a number of software applications,modules, services or other elements located within at least one workingmemory device, including an operating system and application programs,such as a client application or browser. It should be appreciated thatalternate embodiments may have numerous variations from that describedabove. F or example, customized hardware might also be used and/orparticular elements might be implemented in hardware, software(including portable software, such as applets) or both. Further,connections to other computing devices such as network input/outputdevices may be employed.

Numerous specific details are set forth herein to provide a thoroughunderstanding of the claimed subject matter. However, those skilled inthe art will understand that the claimed subject matter may be practicedwithout these specific details. In other instances, methods,apparatuses, or systems that would be known by one of ordinary skillhave not been described in detail so as not to obscure claimed subjectmatter. The various embodiments illustrated and described are providedmerely as examples to illustrate various features of the claims.However, features shown and described with respect to any givenembodiment are not necessarily limited to the associated embodiment andmay be used or combined with other embodiments that are shown anddescribed. Further, the claims are not intended to be limited by any oneexample embodiment.

While the present subject matter has been described in detail withrespect to specific embodiments thereof, it will be appreciated thatthose skilled in the art, upon attaining an understanding of theforegoing may readily produce alterations to, variations of, andequivalents to such embodiments. Accordingly, it should be understoodthat the present disclosure has been presented for purposes of examplerather than limitation, and does not preclude inclusion of suchmodifications, variations, and/or additions to the present subjectmatter as would be readily apparent to one of ordinary skill in the art.Indeed, the methods and systems described herein may be embodied in avariety of other forms; furthermore, various omissions, substitutionsand changes in the form of the methods and systems described herein maybe made without departing from the spirit of the present disclosure. Theaccompanying claims and their equivalents are intended to cover suchforms or modifications as would fall within the scope and spirit of thepresent disclosure.

Although the present disclosure provides certain example embodiments andapplications, other embodiments that are apparent to those of ordinaryskill in the art, including embodiments which do not provide all of thefeatures and advantages set forth herein, are also within the scope ofthis disclosure. Accordingly, the scope of the present disclosure isintended to be defined only by reference to the appended claims.

Unless specifically stated otherwise, it is appreciated that throughoutthis specification discussions utilizing terms such as “processing,”“computing,” “calculating,” “determining,” and “identifying” or the likerefer to actions or processes of a computing device, such as one or morecomputers or a similar electronic computing device or devices, thatmanipulate or transform data represented as physical electronic ormagnetic quantities within memories, registers, or other informationstorage devices, transmission devices, or display devices of thecomputing platform.

The system or systems discussed herein are not limited to any particularhardware architecture or configuration. A computing device can includeany suitable arrangement of components that provide a result conditionedon one or more inputs. Suitable computing devices include multi-purposemicroprocessor-based computer systems accessing stored software thatprograms or configures the computing system from a general purposecomputing apparatus to a specialized computing apparatus implementingone or more embodiments of the present subject matter. Any suitableprogramming, scripting, or other type of language or combinations oflanguages may be used to implement the teachings contained herein insoftware to be used in programming or configuring a computing device.

Embodiments of the methods disclosed herein may be performed in theoperation of such computing devices. The order of the blocks presentedin the examples above can be varied—for example, blocks can bere-ordered, combined, and/or broken into sub-blocks. Certain blocks orprocesses can be performed in parallel.

Conditional language used herein, such as, among others, “can,” “could,”“might,” “may,” “e.g.,” and the like, unless specifically statedotherwise, or otherwise understood within the context as used, isgenerally intended to convey that certain examples include, while otherexamples do not include, certain features, elements, and/or steps. Thus,such conditional language is not generally intended to imply thatfeatures, elements and/or steps are in any way required for one or moreexamples or that one or more examples necessarily include logic fordeciding, with or without author input or prompting, whether thesefeatures, elements and/or steps are included or are to be performed inany particular example.

The terms “comprising,” “including,” “having,” and the like aresynonymous and are used inclusively, in an open-ended fashion, and donot exclude additional elements, features, acts, operations, and soforth. Also, the term “or” is used in its inclusive sense (and not inits exclusive sense) so that when used, for example, to connect a listof elements, the term “or” means one, some, or all of the elements inthe list. The use of “adapted to” or “configured to” herein is meant asopen and inclusive language that does not foreclose devices adapted toor configured to perform additional tasks or steps. Additionally, theuse of “based on” is meant to be open and inclusive, in that a process,step, calculation, or other action “based on” one or more recitedconditions or values may, in practice, be based on additional conditionsor values beyond those recited. Similarly, the use of “based at least inpart on” is meant to be open and inclusive, in that a process, step,calculation, or other action “based at least in part on” one or morerecited conditions or values may, in practice, be based on additionalconditions or values beyond those recited. Headings, lists, andnumbering included herein are for ease of explanation only and are notmeant to be limiting.

The various features and processes described above may be usedindependently of one another, or may be combined in various ways. Allpossible combinations and sub-combinations are intended to fall withinthe scope of the present disclosure. In addition, certain method orprocess blocks may be omitted in some embodiments. The methods andprocesses described herein are also not limited to any particularsequence, and the blocks or states relating thereto can be performed inother sequences that are appropriate. For example, described blocks orstates may be performed in an order other than that specificallydisclosed, or multiple blocks or states may be combined in a singleblock or state. The example blocks or states may be performed in serial,in parallel, or in some other manner. Blocks or states may be added toor removed from the disclosed examples. Similarly, the example systemsand components described herein may be configured differently thandescribed. For example, elements may be added to, removed from, orrearranged compared to the disclosed examples.

What is claimed is:
 1. A computer-implemented method comprising:controlling, by one or more processors, an optical sensor disposed on acomputer peripheral device, the optical sensor configured to generatemovement data at intervals, the movement data corresponding to a trackedmovement of the computer peripheral device with respect to an underlyingsurface; sending, by the one or more processors, a first report to areceiver coupled to a host computing device at a first report rate, thefirst report including one or more intervals of generated movement data,the first report being sent via a wireless communications protocol;receiving the first report by the receiver; computing, by the receiver,a current velocity and acceleration of the computer peripheral devicebased on the one or more intervals of movement data in the first report;predicting, by the receiver, a trajectory of the computer peripheraldevice based on the first report; computing, by the receiver, anincremental displacement of the computer peripheral device based on thepredicted trajectory; generating, by the receiver, data indicative of aposition or displacement of the computer peripheral device based on thecomputed current velocity and acceleration of the computer peripheraldevice; and sending, by the receiver to a host computing device, thedata indicative of a position or displacement of the computer peripheraldevice.
 2. The method of claim 1 wherein the predicting the trajectoryof the computer peripheral device is performed by a state estimatoroperating on the receiver.
 3. The method of claim 2 wherein the stateestimator is a linear state estimator.
 4. The method of claim 2 whereinthe state estimator incorporates an extended Kalman filter configured toincorporate an estimated error to balance error correction of thecomputed current velocity and acceleration of the computer peripheraldevice.
 5. The method of claim 1 wherein the first report rate is 6 msor faster.
 6. The method of claim 1 further comprising: controlling, bythe one or more processors, an inertial measurement unit (IMU) on thecomputer peripheral device, the IMU configured to generate accelerationdata corresponding to the tracked movement of the computer peripheraldevice with respect to the underlying surface.
 7. The method of claim 1wherein the peripheral computing device is a computer mouse, and whereinthe receiver is a USB dongle physically and communicatively coupled tothe host computing device.
 8. A computer-implemented method comprising:receiving a first report from a computer peripheral device by a receiverat a first report rate, the first report including one or more intervalsof movement data generated by the computer peripheral device, themovement data corresponding to a tracked movement of the computerperipheral device with respect to an underlying surface, and the firstreport being received via a wireless communications protocol; computing,by the receiver, a current trajectory of the computer peripheral devicebased on the one or more intervals of movement data in the first report;computing, by the receiver, a predicted trajectory of the computerperipheral device based on the first report; computing, by the receiver,an incremental displacement of the computer peripheral device based onthe predicted trajectory; generating, by the receiver, data indicativeof a position or displacement of the computer peripheral device based onthe predicted trajectory of the computer peripheral device; and sending,by the receiver to a host computing device, the data indicative of aposition or displacement of the computer peripheral device.
 9. Themethod of claim 8 wherein the movement data includes velocity oracceleration data, and the current trajectory is computed based on thevelocity or acceleration data.
 10. The method of claim 8 wherein thecomputing the predicted trajectory of the computer peripheral device isperformed by a state estimator operating on the receiver.
 11. The methodof claim 10 wherein the state estimator is a linear state estimator, aKalman filter, or an extended Kalman filter configured to incorporate anestimated error to balance error correction of the computed currenttrajectory of the computer peripheral device.
 12. The method of claim 8wherein the first report rate is 6 ms or faster.
 13. The method of claim8 further comprising: controlling an inertial measurement unit (IMU) onthe computer peripheral device, the IMU configured to generateacceleration data corresponding to the tracked movement of the computerperipheral device with respect to the underlying surface.
 14. The methodof claim 8 wherein the peripheral computing device is a computer mouse,and wherein the receiver is a USB dongle physically and communicativelycoupled to the host computing device.
 15. A system comprising: one ormore processors; and one or more machine-readable, non-transitorystorage mediums that include instructions configured to cause the one ormore processors to perform operations including: receiving a firstreport from a computer peripheral device by a receiver at a first reportrate, the first report including one or more intervals of movement datagenerated by the computer peripheral device, the movement datacorresponding to a tracked movement of the computer peripheral devicewith respect to an underlying surface, and the first report beingreceived via a wireless communications protocol; computing, by thereceiver, a predicted trajectory of the computer peripheral device basedon the one or more intervals of movement data in the first report;computing, by the receiver, a predicted trajectory of the computerperipheral device based on the first report; computing, by the receiver,an incremental displacement of the computer peripheral device based onthe predicted trajectory; generating, by the receiver, data indicativeof a position or displacement of the computer peripheral device based onthe predicted trajectory of the computer peripheral device; and sending,by the receiver to a host computing device, the data indicative of aposition or displacement of the computer peripheral.
 16. The system ofclaim 15 wherein the movement data includes velocity or accelerationdata, and the predicted trajectory is computed based on the velocity oracceleration data.
 17. The system of claim 15 wherein the computing thepredicted trajectory of the computer peripheral device is performed by astate estimator operating on the receiver, which includes at least oneof a linear state estimator, a Kalman filter, or an extended Kalmanfilter configured to incorporate an estimated error to balance errorcorrection of the computed predicted trajectory of the computerperipheral device.
 18. The system of claim 15 wherein the first reportrate is 6 ms or faster.
 19. The system of claim 15 further comprising:controlling, by the one or more processors, an inertial measurement unit(IMU) on the computer peripheral device, the IMU configured to generateacceleration data corresponding to the tracked movement of the computerperipheral device with respect to the underlying surface.
 20. The systemof claim 15 wherein the peripheral computing device is a computer mouse,and wherein the receiver is a USB dongle physically and communicativelycoupled to the host computing device.